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This article explores the application of image processing techniques in recyclable waste paper sorting. In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams facilitate high quality end products and save processing chemicals and energy. From 1932 to 2009, different mechanical and optical paper sorting methods have been developed to fill the paper sorting demand. Still, in many countries including Malaysia, waste papers are sorted into different grades using a manual sorting system. Because of inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. Automated paper sorting systems offer significant advantages over human inspection in terms of worker fatigue, throughput, speed, and accuracy. This research attempts to develop a smart vision sensing system that is able to separate the different grades of paper using first-order features. To construct a template database, a statistical approach with intra-class and inter-class variation techniques are applied to the feature selection process. Finally, the K-nearest neighbor (KNN) algorithm is applied for paper object grade identification. The remarkable achievement obtained with the method is the accurate identification and dynamic sorting of all grades of papers using simple image processing techniques.

Feature selection determines relevant features in the data. It is often applied in pattern classification, data mining, as well as machine learning. A special concern for feature selection nowadays is that the size of a database is normally very large, both vertically and horizontally. In addition, feature sets may grow as the data collection process continues. Effective solutions are needed to accommodate the practical demands. This paper concentrates on three issues: large number of features, large data size, and expanding feature set. For the first issue, we suggest a probabilistic algorithm to select features. For the second issue, we present a scalable probabilistic algorithm that expedites feature selection further and can scale up without sacrificing the quality of selected features. For the third issue, we propose an incremental algorithm that adapts to the newly extended feature set and captures `concept drifts' by removing features from previously selected and newly added ones. We expect that research on scalable feature selection will be extended to distributed and parallel computing and have impact on applications of data mining and machine learning.

In statistics, machine learning, and related fields, feature selection is the process of choosing a smaller subset of features to work with. This is an important topic since selecting a subset of features can help analysts to interpret models and data, and to decrease computational runtimes. While many techniques are purely automatic, the data visualization community has produced a number of interactive approaches where users can make decisions taking into account their domain knowledge. In this paper we propose a new visualization technique based on radial axes that allows analysts to perform feature selection effectively, in contrast to previous radial axes methods. This is achieved by employing alternative scaled axes that provide insight regarding the features that have a smaller contribution to the visualizations. Therefore, analysts can use the technique to carry out interactive backwards feature elimination, by discarding the least relevant features according to the information on the plots and their expertise. Our approach can be coupled with any linear dimensionality reduction method, and can be used when performing analyses of cluster structure, correlations, class separability, etc. Specifically, in this paper we focus on combining the proposed technique with methods designed for classification. Lastly, we illustrate the effectiveness of our proposal through a case study analyzing high-dimensional medical chronic conditions data. In particular, clinicians have used the technique for determining the most important features that discriminate between patients with diabetes and high blood pressure.

Proposed a new sentiment analysis method, based on text-based hidden Markov models, that uses word orders without the need of sentiment lexicons.Proposed an ensemble of text-based hidden Markov models using boosting and clusters of words produced by latent semantic analysis.Showed the method has potential to classify implicit opinions by the proposed ensemble method.Showed better performance in comparison to several previous algorithms in several datasets.Applied it to a real-life dataset to classify paper titles. With the rapid growth of social media, text mining is extensively utilized in practical fields, and opinion mining, also known as sentiment analysis, plays an important role in analyzing opinion and sentiment in texts. Methods in opinion mining generally depend on a sentiment lexicon, which is a set of predefined key words that express sentiment. Opinion mining requires proper sentiment words to be extracted in advance and has difficulty classifying sentences that imply an opinion without using any sentiment key words. This paper presents a new sentiment analysis method, based on text-based hidden Markov models (TextHMMs), for text classification that uses a sequence of words in training texts instead of a predefined sentiment lexicon. We sought to learn text patterns representing sentiment through ensemble TextHMMs. Our method defines hidden variables in TextHMMs by semantic cluster information in consideration of the co-occurrence of words, and thus calculates the sentiment orientation of sentences by fitted TextHMMs. To reflect diverse patterns, we applied an ensemble of TextHMM-based classifiers. In the experiments with a benchmark data set, we show that this method is superior to some existing methods and particularly has potential to classify implicit opinions. We also demonstrate the practicality of the proposed method in a real-life data set of online market reviews.

Digital pre-assembly process can evaluate flaws in design prior to manufacturing.As these reports are written in free text form, they are not fully utilized.This paper proposes a method of applying text mining techniques to extract insights.The proposed method successfully extracts useful information from the text database. After digitally designing components of vehicles, a design team creates a virtual manufacturing environment that resembles actual manufacturing facilities. During this digital pre-assembly process, a review team examines each component, and records its problems and requirements in part verification reports. Once these reports are delivered to specific design team responsible for each part, the design team can make appropriate adjustments to their designs. This digital pre-assembly process can evaluate and prevent flaws in design prior to actual manufacturing, improving production quality and reducing manufacturing cost. As these reports are written in free text form, they, however, are not fully utilized for understanding problems arising from the design process. This paper proposes a method of applying text mining techniques on verification reports to extract insights for quality improvement. In this paper, following three text mining approaches are proposed: (1) Extracting n-grams for text preprocessing and constructing domain ontology; (2) Extracting meaningful insights from text preprocessing; (3) Creating intuitive visual tools to understand the extracted insights. The proposed method is applied on approximately 140,000 reports, and is validated through the quality of the answers obtained for the questions posed by the domain experts. The proposed method successfully extracts useful information from the text database, and provides intuitive graphical interface, thereby satisfying the need of the domain experts. This paper proposes a systematic framework of transforming huge amount of raw text data into intuitive visualization. Through this framework, meaningful knowledge can be extracted, analyzed and shared to improve the quality of the products. Main contribution of our paper is that it proposes a framework for knowledge extraction from pre-assembly process. Not only does it systematically arrange the data, but it also combines various data sources and creates a knowledge system to improve efficiency of the design process.

We propose estimating the quality of a contrast pattern using unseen objects.We evaluate quality measures by correlating their values and the estimated quality.We describe the influence of database characteristics in correlations.We perform a study to find how to synergistically combine quality measures. Contrast patterns, which lie in the core of most understandable classifiers, are frequently evaluated by quality measures. Since many different quality measures are available, they should be compared to select the most appropriate for each applications. This paper introduces a method to compare quality measures, using a set of mined patterns and a collection of objects not used for mining. The comparison is performed by correlating quality values with a quality estimation of the patterns. Additionally, a meta-learning study is performed to show that combining quality measures could be better than using the best single measures in isolation. The results of this paper can help researchers to create new quality measures or to find new combinations of quality measures to create better understandable classification systems.

Traditional social community discovery methods concentrate mainly on static social networks, but the analysis of dynamic networks is a prerequisite for real-time and personalized social services. Through the study of community changes, the community structure in a dynamic network can be tracked over time, which helps in the mining of dynamic network information. In this paper, we propose a method of tracking dynamic community evolution that is based on resistance distance. Specifically, we model the time-varying features of dynamic networks using the convergence of a resistance-based distance. In our model, the heterogeneity of neighboring nodes can be obtained in the local topology of nodes by analyzing the resistance distance between nodes. We design a community discovery algorithm that essentially discovers community structures on dynamic networks by identifying the so-called core node. During the process of community evolution analysis, both the dynamic contribution of ordinary nodes and core nodes in each community are considered. In addition, to avoid the inclusion of spurious communities in the community structure, we define the notion of noise community and account for it in our algorithm. Experimental results show that the method proposed in this paper can yield better accuracy than other existing methods.

Solving a real-world problem with an uncommon dataset.Highly time-efficient and accurate for classification of high resolution X-Ray images.Emphasizing on keypoints of the image instead of considering all parts of the image.Considering the dependency between the visual words in the bag of visual words.Adopting Tree-Augmented Bayes in the task of image classification. Smuggling has long played an important role in the inefficiency of economies. To secure the borders against this illegal act, X-Ray Inspection Systems are often deployed at the borders and customs. In this paper, we present a new method for classification of shipping containers X-Ray images, produced in the inspection lines. The aim is to improve the matching accuracy of the presented manifest, which lists the claimed contents of the shipping containers, with the real contents of the container. The proposed method is based on utilizing Scale Invariant Feature Transforms (SIFT) feature vectors, Bag of visual words (BOVW) and tree augmented naive Bayes (TAN) approach for classifying containers X-Ray images. The prior research on classification of X-Ray images of shipping containers has focused mostly on working with greedy algorithms such as sliding windows for task of classification. More recent studies introduced filter banks and visual words for extraction of features. The proposed method for the first time considers the salient points and keypoints for the task of feature extraction. In addition, this paper presents a framework using the tree augmented naive Bayes based on the theory of learning Bayesian networks, which is proved to have a significant improvements upon the prior designed systems by considering the correlations among the extracted features. For experimental evaluations, our method is compared with two recently proposed methods on containers X-Ray images categorization. The results show that the proposed method is more accurate and time-efficient in categorization of X-Ray images.

A taxonomy that classifies ensemble models in the literature is presented.Surface and deep features integration is explored to improve classification.Several ensembles of classifiers and features are proposed and evaluated.Performance of the proposed models is evaluated on several sentiment datasets. Deep learning techniques for Sentiment Analysis have become very popular. They provide automatic feature extraction and both richer representation capabilities and better performance than traditional feature based techniques (i.e., surface methods). Traditional surface approaches are based on complex manually extracted features, and this extraction process is a fundamental question in feature driven methods. These long-established approaches can yield strong baselines, and their predictive capabilities can be used in conjunction with the arising deep learning methods. In this paper we seek to improve the performance of deep learning techniques integrating them with traditional surface approaches based on manually extracted features. The contributions of this paper are sixfold. First, we develop a deep learning based sentiment classifier using a word embeddings model and a linear machine learning algorithm. This classifier serves as a baseline to compare to subsequent results. Second, we propose two ensemble techniques which aggregate our baseline classifier with other surface classifiers widely used in Sentiment Analysis. Third, we also propose two models for combining both surface and deep features to merge information from several sources. Fourth, we introduce a taxonomy for classifying the different models found in the literature, as well as the ones we propose. Fifth, we conduct several experiments to compare the performance of these models with the deep learning baseline. For this, we use seven public datasets that were extracted from the microblogging and movie reviews domain. Finally, as a result, a statistical study confirms that the performance of these proposed models surpasses that of our original baseline on F1-Score.

A new initialization technique is proposed to improve the performance of K-means.A data transformation approach is proposed to solve empty cluster problem.An efficient method is proposed to estimate the optimal number of clusters.Proposed clustering method provides more accurate clustering results. Clustering provides a knowledge acquisition method for intelligent systems. This paper proposes a novel data-clustering algorithm, by combining a new initialization technique, K-means algorithm and a new gradual data transformation approach to provide more accurate clustering results than the K-means algorithm and its variants by increasing the clusters coherence. The proposed data transformation approach solves the problem of generating empty clusters, which frequently occurs for other clustering algorithms. An efficient method based on the principal component transformation and a modified silhouette algorithm is also proposed in this paper to determine the number of clusters. Several different data sets are used to evaluate the efficacy of the proposed method to deal with the empty cluster generation problem and its accuracy and computational performance in comparison with other K-means based initialization techniques and clustering methods. The developed estimation method for determining the number of clusters is also evaluated and compared with other estimation algorithms. Significances of the proposed method include addressing the limitations of the K-means based clustering and improving the accuracy of clustering as an important method in the field of data mining and expert systems. Application of the proposed method for the knowledge acquisition in time series data such as wind, solar, electric load and stock market provides a pre-processing tool to select the most appropriate data to feed in neural networks or other estimators in use for forecasting such time series. In addition, utilization of the knowledge discovered by the proposed K-means clustering to develop rule based expert systems is one of the main impacts of the proposed method.

Novel fuzzy rule-based system (FRBS) for medical data classification tasks is proposed.FRBSs are designed using multi-objective evolutionary optimization algorithms.Set of FRBSs with various levels of accuracy-interpretability trade-off is generated.Benchmark medical data sets are used to evaluate the effectiveness of the system.Highly interpretable and accurate medical decision support is provided by our approach. In medical decision support systems, both the accuracy (i.e., the ability to adequately represent the decision making processes) as well as the transparency and interpretability (i.e., the ability to provide a domain user with compact and understandable explanation and justification of the proposed decisions) play essential roles. This paper presents an approach for automatic design of fuzzy rule-based classification systems (FRBCSs) from medical data using multi-objective evolutionary optimization algorithms (MOEOAs). Our approach generates, in a single run, a collection of solutions (medical FRBCSs) characterized by various levels of accuracy-interpretability trade-off. We propose a new complexity-related interpretability measure and we address the semantics-related interpretability issue by means of efficient implementation of the so-called strong fuzzy partitions of attribute domains. We also introduce a special-coding-free representation of the rule base and original genetic operators for its processing as well as we implement our ideas in the context of well-known and one of the presently most advanced MOEOAs, i.e., Non-dominated Sorting Genetic Algorithm II (NSGA-II). An important part of the paper is devoted to a broad comparative analysis of our approach and as many as 26 alternative techniques arranged in 32 experimental set-ups and applied to three well-known benchmark medical data sets (Breast Cancer Wisconsin (Original), Pima Indians Diabetes, and Heart Disease (Cleveland)) available from the UCI repository of machine learning databases (http://archive.ics.uci.edu/ml). A number of useful in medical applications performance measures including accuracy, sensitivity, specificity, and several interpretability measures are employed. The results of such a broad comparative analysis demonstrate that our approach significantly outperforms the alternative methods in terms of the interpretability of the obtained FRBCSs while remaining either competitive or superior in terms of their accuracy. It is worth stressing that the overwhelming majority of the existing medical classification methods concentrate almost exclusively on the accuracy issues.

Four methods are developed for data mining discrete multi-objective optimization datasets.Two of the methods are unsupervised, one is supervised and the other is hybrid.Knowledge is represented as patterns in one method, and as rules in other methods.Methods are applied to three real-world production system optimization problems.Extracted knowledge is compared across methods and provides new insights. The first part of this paper served as a comprehensive survey of data mining methods that have been used to extract knowledge from solutions generated during multi-objective optimization. The current paper addresses three major shortcomings of existing methods, namely, lack of interactiveness in the objective space, inability to handle discrete variables and inability to generate explicit knowledge. Four data mining methods are developed that can discover knowledge in the decision space and visualize it in the objective space. These methods are (i) sequential pattern mining, (ii) clustering-based classification trees, (iii) hybrid learning, and (iv) flexible pattern mining. Each method uses a unique learning strategy to generate explicit knowledge in the form of patterns, decision rules and unsupervised rules. The methods are also capable of taking the decision makers preferences into account to generate knowledge unique to preferred regions of the objective space. Three realistic production systems involving different types of discrete variables are chosen as application studies. A multi-objective optimization problem is formulated for each system and solved using NSGA-II to generate the optimization datasets. Next, all four methods are applied to each dataset. In each application, the methods discover similar knowledge for specified regions of the objective space. Overall, the unsupervised rules generated by flexible pattern mining are found to be the most consistent, whereas the supervised rules from classification trees are the most sensitive to user-preferences.

In this research UCI Indian Liver Patient Dataset (ILPD) used.Boosted C5.0 and CHAID algorithms are used to identify liver disease risk factors.This research shows females have more chance of liver disease than males.Common risk factors of liver disease were extracted by data mining.This research produced quite simple rules. The human liver is one of the major organs in the body and liver disease can cause many problems in human life. Fast and accurate prediction of liver disease allows early and effective treatments. In this regard, various data mining techniques help in better prediction of this disease. Because of the importance of liver disease and increase the number of people who suffer from this disease, we studied on liver disease through using two well-known methods in data mining area.In this paper, novel decision tree based algorithms is used which leads to considering more factors in general and predictions with high accuracy compared to other studies in liver disease. In this application, 583 UCI instances of liver disease dataset from the UCI repository are considered. This dataset consists of 416 records of liver disease and 167 records of healthy liver. This dataset is analyzed by two algorithms named Boosted C5.0 and CHAID algorithms. Until now there is no work in the literature that uses boosted C5.0 and CHAID for creating the rules in liver disease. Our results show that in both algorithms, the DB, ALB, SGPT, TB and A/G factors have a significant impact on predicting liver disease which according to the rules generated by both algorithms important ranges are DB=10.900-1.200, ALB 4.00-4.300, SGPT=34-37, TB=0.600-1.200 (by boosted C5.0), A/G=1.180-1.390, as well as in the Boosted C5.0 algorithm, Alkphos, SGOT and Age have significant impact in prediction of liver disease. By comparing the performance of these algorithms, it becomes clear that C5.0 algorithm via Boosting technique has an accuracy of 93.75% and this result reveals that it has a better performance than the CHAID algorithm which is 65.00%. Another important achievement of this paper is about the ability of both algorithms to produce rules in one class for liver disease. The results of our assessment show that Boosted C5.0 and CHAID algorithms are capable to produce rules for liver disease. Our results also show that boosted C5.0 considers the gender in liver disease, a factor which is missing in many other studies. Meanwhile, using the rules generated in boosted C5.0 algorithm, we obtained the important result about low susceptibility of female to liver disease than male. This factor is missing in other studies of liver disease. Therefore, our proposed computer-aided diagnostic methods as an expert and intelligent system have impressive impact on liver disease detection. Based on obtained results, we observed that our model had better performance compared to existing methods in the literature.

The adaptive rule-based classifier is used to classify multi-class biological data.It applies random subspace and boosting approaches with ensemble of decision trees.Decision tree induction is used for evolving rules from the biological data.k-nearest-neighbor is used for removing ambiguity between the contradictory rules.The classifier is evaluated using 148 Exome data sets and 10 life sciences data sets. In this paper, we introduce a new adaptive rule-based classifier for multi-class classification of biological data, where several problems of classifying biological data are addressed: overfitting, noisy instances and class-imbalance data. It is well known that rules are interesting way for representing data in a human interpretable way. The proposed rule-based classifier combines the random subspace and boosting approaches with ensemble of decision trees to construct a set of classification rules without involving global optimisation. The classifier considers random subspace approach to avoid overfitting, boosting approach for classifying noisy instances and ensemble of decision trees to deal with class-imbalance problem. The classifier uses two popular classification techniques: decision tree and k-nearest-neighbor algorithms. Decision trees are used for evolving classification rules from the training data, while k-nearest-neighbor is used for analysing the misclassified instances and removing vagueness between the contradictory rules. It considers a series of k iterations to develop a set of classification rules from the training data and pays more attention to the misclassified instances in the next iteration by giving it a boosting flavour. This paper particularly focuses to come up with an optimal ensemble classifier that will help for improving the prediction accuracy of DNA variant identification and classification task. The performance of proposed classifier is tested with compared to well-approved existing machine learning and data mining algorithms on genomic data (148 Exome data sets) of Brugada syndrome and 10 real benchmark life sciences data sets from the UCI (University of California, Irvine) machine learning repository. The experimental results indicate that the proposed classifier has exemplary classification accuracy on different types of biological data. Overall, the proposed classifier offers good prediction accuracy to new DNA variants classification where noisy and misclassified variants are optimised to increase test performance.

We describe method for purifying category structure of Wikipedia.We propose three methods for identification of associations between categories using SVM.Evaluation indicates the methods allows us to identify new significant relations.The aggregated results have been used for visualization of Wikipedia categories. Description of the data using categories allows one to describe it on a higher abstraction level. In this way, we can operate on aggregated groups of the information, allowing one to see relationships that do not appear explicit when we analyze the individual objects separately. In this paper we present automatic identification of the associations between categories used for organization of the textual data. As experimental data we used a network of English Wikipedia articles and their associated categories, that have been preprocessed by a dedicated filtering method for noise reduction. The main contribution of the paper is the introduction of the method based on supervised machine learning for mining relations between these categories. We describe existing in the literature category proximity metrics as well as introduce three new ones, based on observing the properties of a multilabel Support Vector Machine classifier. The first metric uses classifier predictions, the second uses its errors, and the third is based on its model. Comparison to the existing state-of-the-art methods, and to manual assessments, confirm that the proposed methods are useful and are more flexible than typical approaches. We show how different metrics allow us to introduce new significant relations between categories. Aggregated results of mining categories' associations have been used to build a semantic network that shows a practical application of the research. The proposed method for finding associations can be extended with using other approaches than SVM classification, and can find (other than presented in the paper) applications for mining categories in text repositories. Eg.: it can be used for extending the prediction of the rating in recommender systems or as a method of missing data imputation.

In this paper, a case study on the role of fuzzy logic (FL) in data mining and machine learning is carried out. It is outlined that, in order to draw more attention of data-mining and machine-learning communities to FL, studies on FL could be more focused not on the activities that fuzzy methods can perform better but rather on the activities that fuzzy methods can perform and the non-fuzzy ones can’t. Such approach takes us away from discussing quantitative differences between fuzzy and non-fuzzy methods to discussing qualitative differences, which are possibly more favorable objects of scientific curiosity. Following the outlined suggestion, a novel speed-up technique is proposed in this paper to support association rule mining (ARM). The proposed technique is a clustering-based one and provides fusion of clustering and ARM. The catchy feature of this technique is that it works well if applied in fuzzy ARM and doesn’t work well if applied in non-fuzzy ARM. The proposed technique is put through experimental verification involving several real-world datasets, and the results substantiate its effectiveness.

This paper shows a system for feature optimization using modified Taguchi method.This method can reduce the number of features, and classification hazards.This study enhances the rules of Taguchi method for the system has no exact output.This study classifies multiple clusters with less number of parameters.This method possesses minimal pre-processing with anchor point feature selection. Development of an expert system for clinical application includes automation in diagnosis of abnormality and patient monitoring based on features derived from continuous data set. This paper presents a novel method for feature optimization and classification of electrocardiogram (ECG) for arrhythmia analysis. A feature set optimization technique can reduce the classification hazard by selecting few comprehensive features to cater all kind of abnormalities under consideration. Proposed work deals with ranking and selection of an optimized pair of features using Taguchi method from eleven possible features normally used for characterizing arrhythmic beats like left bundle branch (LBBB), right bundle branch (RBBB) and premature ventricular contraction (PVC) are compared to normal beats. An imposed target based modification of Taguchi method is also suggested for the systems where the output is not pre-defined as in the case of biomedical applications. The proposed method is advantageous for the expert systems in which individual identity of the features are to be stored while reducing the dimensionality of the feature set. Multiclass Navis Bayes classifier is used to classify the beats in a single run and good performance parameters are obtained as reported in the result section.

A new technique for instance selection and noise filtering for regression is proposed.The method use instance selection for classification after output value discretization.The method is much simpler and more robust to noise than other specifically designed for regression. An important step in building expert and intelligent systems is to obtain the knowledge that they will use. This knowledge can be obtained from experts or, nowadays more often, from machine learning processes applied to large volumes of data. However, for some of these learning processes, if the volume of data is large, the knowledge extraction phase is very slow (or even impossible). Moreover, often the origin of the data sets used for learning are measure processes in which the collected data can contain errors, so the presence of noise in the data is inevitable. It is in such environments where an initial step of noise filtering and reduction of data set size plays a fundamental role. For both tasks, instance selection emerges as a possible solution that has proved to be useful in various fields. In this paper we focus mainly on instance selection for noise removal. In addition, in contrast to most of the existing methods, which applied instance selection to classification tasks (discrete prediction), the proposed approach is used to obtain instance selection methods for regression tasks (prediction of continuous values). The different nature of the value to predict poses an extra difficulty that explains the low number of articles on the subject of instance selection for regression.More specifically the idea used in this article to adapt to regression problems "classic" instance-selection algorithms for classification is as simple as the discretization of the numerical output variable. In the experimentation, the proposed method is compared with much more sophisticated methods, specifically designed for regression, and shows to be very competitive.The main contributions of the paper include: (i) a simple way to adapt to regression instance selection algorithms for classification, (ii) the use of this approach to adapt a popular noise filter called ENN (edited nearest neighbor), and (iii) the comparison of this noise filter against two other specifically designed for regression, showing to be very competitive despite its simplicity.

This paper proposes a method to improve neural network classification performance.A linear model was used as post processing of neural network.The parameters of linear model was estimated using Kalman filter iteration.The method can be applied to classify an object regardless of the type of feature.The method has been validated with five different datasets. Neural network has been applied in several classification problems such as in medical diagnosis, handwriting recognition, and product inspection, with a good classification performance. The performance of a neural network is characterized by the neural network's structure, transfer function, and learning algorithm. However, a neural network classifier tends to be weak if it uses an inappropriate structure. The neural network's structure depends on the complexity of the relationship between the input and the output. There are no exact rules that can be used to determine the neural network's structure. Therefore, studies in improving neural network classification performance without changing the neural network's structure is a challenging issue. This paper proposes a method to improve neural network classification performance by constructing a linear model based on the Kalman filter as a post processing. The linear model transforms the predicted output of the neural network to a value close to the desired output by using the linear combination of the object features and the predicted output. This simple transformation will reduce the error of neural network and improve classification performance. The Kalman filter iteration is used to estimate the parameters of the linear model. Five datasets from various domains with various characteristics, such as attribute types, the number of attributes, the number of samples, and the number of classes, were used for empirical validation. The validation results show that the linear model based on the Kalman filter can improve the performance of the original neural network.

We concentrated on the dental X-ray image segmentation problem.A new framework combining Otsu, FCM and semi-supervised fuzzy clustering was shown.It was tested on real datasets from Hanoi Medical University in terms of accuracy.The new framework has better performance than the relevant methods.Suggestions on means and variances of the criteria of the new framework were made. Dental X-ray image segmentation (DXIS) is an indispensable process in practical dentistry for diagnosis of periodontitis diseases from an X-ray image. It has been said that DXIS is one of the most important and necessary steps to analyze dental images in order to get valuable information for medical diagnosis support systems and other recognition tools. Specialized data mining methods for DXIS have been investigated to achieve high accuracy of segmentation. However, traditional image processing and clustering algorithms often meet challenges in determining parameters or common boundaries of teeth samples. It was shown that performance of a clustering algorithm is enhanced when additional information provided by users is attached to inputs of the algorithm. In this paper, we propose a new cooperative scheme that applies semi-supervised fuzzy clustering algorithms to DXIS. Specifically, the Otsu method is used to remove the Background area from an X-ray dental image. Then, the FCM algorithm is chosen to remove the Dental Structure area from the results of the previous steps. Finally, Semi-supervised Entropy regularized Fuzzy Clustering algorithm (eSFCM) is opted to clarify and improve the results based on the optimal result from the previous clustering method. The proposed framework is evaluated on a real collection of dental X-ray image datasets from Hanoi Medical University, Vietnam. Experimental results have revealed that clustering quality of the cooperative framework is better than those of the relevant ones. The findings of this paper have great impact and significance to researches in the fields of medical science and expert systems. It has been the fact that medical diagnosis is often an experienced and case-based process which requests long time practicing in real patients. In many situations, young clinicians do not have chance for such the practice so that it is necessary to utilize a computerized medical diagnosis system which could simulate medical processes from previous real evidences. By learning from those cases, clinicians would improve their experience and responses for later ones. In the view of expert systems, this paper made uses of knowledge-based algorithms for a practical application. This shows the advantages of such the algorithm in the conjunction domain between expert systems and medical informatics. The findings also suggested the most appropriate configuration of the algorithm and parameters for this problem that could be reused by other researchers in similar applications. The usefulness and significance of this research are clearly demonstrated within the extent of real-life applications.

Provide knowledge support for quality assurance in the garment industry.Extract relationships among parameters and quality by fuzzy association rule mining.Design novel nature-inspired genetic algorithms with variable lengths.Optimize fuzzy association rules with the use of the slippery genetic algorithm. Due to the error-prone nature of garment manufacturing operations, it is challenging to guarantee the quality of garments. Previous research has been done to apply fuzzy association rule mining to determine process settings for improving the garment quality. The relationship between process parameters and the finished quality is represented in terms of rules. This paper enhances the application by encoding the rules into variable-length chromosomes for optimization with the use of a novel genetic algorithm (GA), namely the slippery genetic algorithm (sGA). Inspired by the biological slippage phenomenon in DNA replication, sGA allows changes to the chromosome lengths by insertion and deletion. During rule optimization, different parameters can be inserted to or removed from a rule, increasing the diversity of the solutions. In this paper, a slippery genetic algorithm-based process mining system (sGAPMS) is developed to optimize fuzzy rules with the aim of facilitating a comprehensive quality assurance scheme in the garment industry. The significance of this paper includes the development of a novel variable-length GA mechanism and the hybridization of fuzzy association rule mining and variable-length GAs. Though the capability of conventional GA in rule optimization has been proven, the diversity in the population is inherently limited by the fixed chromosome length. Motivated by this phenomenon, the sGA suggested in this paper allows various parameters to be considered in a rule, improving the diversity of the solutions. A case study is conducted in a garment manufacturing company to evaluate the sGAPMS. The results illustrate that better quality assurance can be achieved after rule optimization.

Development of an entropy-based evaluation method to evaluate ontology strength.Evaluation an ontological semantic ontology using the evaluation method.Evaluation of the backbone of the UMLS with this method. In this paper we introduce a method to develop knowledge bases for medical decision support systems, with a focus on evaluating such knowledge bases. Departing from earlier efforts with concept maps, we developed an ontological-semantic knowledge base and evaluated its information content using the metrics we have developed, and then compared the results to the UMLS backbone knowledge base. The evaluation method developed uses information entropy of concepts, but in contrast to previous approaches normalizes it against the number of relations to evaluate the information density of knowledge bases of varying sizes. A detailed description of the knowledge base development and evaluation is discussed using the underlying algorithms, and the results of experimentation of the methods are explained. The main evaluation results show that the normalized metric provides a balanced method for assessment and that our knowledge base is strong, despite having fewer relationships, is more information-dense, and hence more useful. The key contributions in the area of developing expert systems detailed in this paper include: (a) introduction of a normalized entropy-based evaluation technique to evaluate knowledge bases using graph theory, (b) results of the experimentation of the use of this technique on existing knowledge bases.

We present complex network model for semantic representation of human language.Contextual scope from texts are inferred from physical measures in complex networks.The complex network modeled presents behavior of scale-free networks.Human language has modular characteristic in the relationship between words.The relationship between words of human language is a sparse space. With increasing amount of information, mainly due to the explosive growth of Internet, the demand for applications of automatic text analysis has also grown. One of the tools that has increased in importance in the understanding of problems related to this area are complex networks. This tool merges graph theory and statistical methods for modeling important problems. In several research fields, complex networks are studied from the various points of view, such as: topology of networks, extraction of physical features and statistics, specific applications, comparison of metrics and study of physical phenomena. Linguistic is one area that has received great attention, particularly due to its close relationship with issues arising from the emergence of large text databases. Thus, many studies have emerged for modeling of complex networks in this area, increasing the demand for efficient algorithms for feature extraction, network dynamic observation and comparison of behavior for different types of languages. Some works for specific languages such as English, Chinese, French, Spanish, Russian and Arabic, have discussed the semantic aspects of these languages. On the other hand, as an important feature of a network we can highlight the computation of average clustering coefficient. This measure has a physical impact on the network topology studies and consequently on the conclusions about the semantics of a language. However its computational time is of O(n3), making its computing prohibitive for large current databases. This paper presents as main contribution a modeling of two complex networks: the first one, in English, is constructed from a specific medical database; the second, in Portuguese, from a journalistic manually annotated database. Our paper then presents the study of the dynamics of these two networks. We show their small-world behavior and the influence of hubs, suggesting that these databases have a high degree of Modularity, indicating specific contexts of words. Also, a method for efficient clustering coefficient computation is presented, and can be applied to large current databases. Other features such as fraction of reciprocal connections and average connection density are also calculated and discussed for both networks.

Alpha profiling reduces the number of comparisons by 85.76%.Optimal features (21 out of 41) are suggested. Features are reduced by 48.78%.Beta profiling is used to reduce the size of training dataset by 7.83%.Network traffic profiling and feature selection reduce space and time complexity.Accuracy of 98.66% and false positive rate of 1.74% are achieved in 2.43 s. Anomaly based Intrusion Detection Systems (IDS) learn normal and anomalous behavior by analyzing network traffic in various benchmark datasets. Common challenges for IDSs are large amounts of data to process, low detection rates and high rates of false alarms. In this paper, a technique based on the Online Sequential Extreme Learning Machine (OS-ELM) is presented for intrusion detection. The proposed technique uses alpha profiling to reduce the time complexity while irrelevant features are discarded using an ensemble of Filtered, Correlation and Consistency based feature selection techniques. Instead of sampling, beta profiling is used to reduce the size of the training dataset. For performance evaluation of proposed technique the standard NSL-KDD 2009 (Network Security Laboratory-Knowledge Discovery and Data Mining) dataset is used. In this paper time and space complexity of the proposed technique is also discussed. The experimental results yielded an accuracy of 98.66% with a false positive rate of 1.74% and a detection time of 2.43 s for binary class NSL-KDD dataset. The proposed IDS achieve 97.67% of accuracy with 1.74% of false positive rate in 2.65 s of detection time for multi-class NSL-KDD dataset. The Kyoto University benchmark dataset is also used to test the proposed IDS. Accuracy of 96.37% with false positive rate of 5.76% is yielded by the proposed technique. The proposed technique outperforms other published techniques in terms of accuracy, false positive rate and detection time. Based on the experimental results achieved, we conclude that the proposed technique is an efficient method for network intrusion detection.

The direction of Borsa Istanbul 100 Index (BIST100) open prices is predicted.A feature selection method, called Balanced Mutual Information (BMI) is proposed.BMI is able to deal with the class imbalance problem through oversampling.BMI is compared with Mutual Information and Chi-square based feature selection.BMI achieves higher macro-averaged F-measure than the other methods using less features. In this paper, a novel method is proposed to predict the direction of Borsa Istanbul (BIST) 100 Index (BIST100) open prices using the news articles released, as well as the price data, from the day before. Although English news articles have been used for market-prediction before, to the best of our knowledge, Turkish news articles together with prices have not yet been used to predict the Turkish markets. Turkish text mining techniques are applied on news articles to form feature vectors for each trading day. The feature vectors are assigned three labels based on the direction of the price change from the closing price of the day before and whether the change is significant. News articles are represented using high dimensional features, some of which could be noisy or irrelevant for prediction. There is also the scarcity of training data. Therefore, this study incorporates feature selection methods to select features that could improve classification performance. By its nature, significant positive or negative changes in stock price happen much less than non-significant changes, resulting in an imbalanced data set. Most feature selection methods in literature aim to reduce the classification accuracy. However, for imbalanced datasets, other measures, such as macro-averaged F-measure need to be considered. The paper proposes a feature selection methods that is able to deal with the class imbalance problem through oversampling of the minority classes and consideration of an ensemble of selected features. In order to decide on importance of features, as the relevance criterion for each feature, the proposed methodology uses mutual information which can detect nonlinear dependencies between variables. Therefore, the proposed feature selection method is called Balanced Mutual Information (BMI) feature selection method. Experiments were performed based on news articles provided by two different news sources: Public Disclosure Platform of BIST and financial news websites. It was shown that, using Balanced Mutual Information feature selection method, the significant changes in the BIST100 Index were predicted with an accuracy of 0.74 and a macro-averaged F-measure of 0.68. The BMI feature selection method was compared with Mutual Information and Chi-square based feature selection methods and it was found out that BMI method results in higher performance using a smaller number of features.

We proposed a novel case adaptation method for wind disaster emergency of power grid.This method improved adaptive multi-objective genetic algorithm in performance.We improved frame theory in incomplete information with evidence theory.This method could be chose as a new method for expanding case base. Case adaptation is a challenging and crucial process of Case-Based Reasoning (CBR) for power grid wind disaster emergencies. The statistical adaptation method is a traditional method that is independent of domain knowledge, is easy to implement, but is not proper for the complex system problem. Therefore, the aim of this paper is to propose a novel case adaptation method to address this problem by integrating the multi-objective genetic algorithm with gray relational analysis, called the grey relational analysis-multi-objective genetic algorithms method (GRAMOGA). Compared with the traditional method, GRAMOGA is performed in terms of corresponding relations between the case similarity and emergency plan, indicating a new idea for case adaptation. To improve adaptation accuracy, this paper improved the multi-objective genetic algorithm by using a selection method based on the fitness function. Furthermore, the frame theory is expanded by combining it with the D/S evidence theory, providing a novel method for case description and retrieval with incomplete information. A practical example from the south of Jiangsu demonstrates that GRAMOGA achieves better adaptation performance for power grid wind disaster emergencies. In addition to the practical applications in case adaptation, GRAMOGA can be used as a novel method for expanding the case base.

Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems., This work is funded by the Portuguese Foundation for Science and Technology (FCT - Fundacao para a Ciencia e a Tecnologia) under research Grant SFRH/BD/84939/2012.

We propose an unsupervised consensus approach for the control chart pattern recognition problem.We provide computational evidence of consensus clustering robustness.We provide motivation for further research on unsupervised learning for the CCPR problem. Early identification and detection of abnormal patterns is vital for a number of applications. In manufacturing for example, slide shifts and alterations of patterns might be indicative of some production process anomaly, such as machinery malfunction. Usually due to the continuous flow of data, monitoring of manufacturing processes and other types of applications requires automated control chart pattern recognition (CCPR) algorithms. Most of the CCPR literature consists of supervised classification algorithms. Fewer studies consider unsupervised versions of the problem. Despite the profound advantage of unsupervised methodology for less manual data labeling their use is limited due to the fact that their performance is not robust enough and might vary significantly from one algorithm to another. In this paper, we propose the use of a consensus clustering framework that takes care of this shortcoming and produces results that are robust with respect to the chosen pool of algorithms. Computational results show that the proposed method achieves not less than 79.10% G-mean with most of test instances achieving higher than 90%. This happens even when in the algorithmic pool are included algorithms with performance less than 15%. To our knowledge, this is the first paper proposing an unsupervised consensus learning approach in CCPR. The proposed approach is promising and provides a new research direction in unsupervised CCPR literature.

We improved wordification methodology and provide a formal framework and pseudo code.We statistically evaluated comparable algorithms on multiple relational databases.Experiments show favorable results in terms of accuracy and efficiency.Feature simplicity is compensated by n-gram construction and by feature weighting.We implemented the full experimental workflow in a data mining platform ClowdFlows. Inductive Logic Programming (ILP) and Relational Data Mining (RDM) address the task of inducing models or patterns from multi-relational data. One of the established approaches to RDM is propositionalization, characterized by transforming a relational database into a single-table representation. This paper presents a propositionalization technique called wordification which can be seen as a transformation of a relational database into a corpus of text documents. Wordification constructs simple, easy to understand features, acting as words in the transformed Bag-Of-Words representation. This paper presents the wordification methodology, together with an experimental comparison of several propositionalization approaches on seven relational datasets. The main advantages of the approach are: simple implementation, accuracy comparable to competitive methods, and greater scalability, as it performs several times faster on all experimental databases. Furthermore, the wordification methodology and the evaluation procedure are implemented as executable workflows in the web-based data mining platform ClowdFlows. The implemented workflows include also several other ILP and RDM algorithms, as well as the utility components that were added to the platform to enable access to these techniques to a wider research audience.

The aim of this study is the development of a weighted consensus clustering that assigns weights to single clustering methods using the purity utility function. In the case of Big data that does not contain labels, the utility function based on the Davies-Bouldin index is proposed in this paper. The Banknote authentication, Phishing, Diabetic, Magic04, Credit card clients, Covertype, Phone accelerometer, and NSL-KDD datasets are used to assess the efficiency of the proposed consensus approach. The proposed approach is evaluated using the Euclidean, Minkowski, squared Euclidean, cosine, and Chebychev distance metrics. It is compared with single clustering algorithms (DBSCAN, OPTICS, CLARANS, k-means, and shared nearby neighbor clustering). The experimental results show the effectiveness of the proposed approach to the Big data clustering in comparison to single clustering methods. The proposed weighted consensus clustering using the squared Euclidean distance metric achieves the highest accuracy, which is a very promising result for Big data clustering. It can be applied to expert systems to help experts make group decisions based on several alternatives. The paper also provides directions for future research on consensus clustering in this area.

This paper aims at identifying next relevant variables for segmentation.Different feature selection techniques are discussed and applied.The performance is assessed using four evaluation metrics for clustering.A new approach is proposed and compared with other candidates.A real application with data from the concert industry is reported in detail. Data mining techniques are widely used by researchers and companies in order to solve problems in a myriad of domains. While these techniques are being adopted and used in daily activities, new operational challenges are encountered concerning the steps following this adoption. In this paper, the problem of updating and improving an existing clustering model by adding relevant new variables is studied. A relevant variable is here defined as a feature which is highly correlated with the current structure of the data, since our main goal is to improve the model by adding new information to the current segmentation, but without modifying it significantly. For this purpose, a general framework is proposed, and subsequently applied in a real business context involving an event organizer facing this problem. Based on extensive experiments based on real data, the performance of the proposed approach is compared to existing methods using different evaluation metrics, leading to the conclusion that the proposed technique is performing better for this specific problem.

Proposed technique use LBP based GLCM feature vector and multiple classifiers.We achieve over 92% accuracy for vegetation classification.Extensive experiments use 5-fold cross validation.The experiments were conducted on dense and sparse grasses.In future, extension will be done by introducing large dataset of grasses. This paper presents a novel texture feature based multiple classifier technique and applies it to roadside vegetation classification. It is well-known that automation of roadside vegetation classification is one of the important issues emerging strongly in improving the fire risk and road safety. Hence, the application presented in this paper is significantly important for identifying fire risks and road safety. The images collected from outdoor environments such as roadside, are affected for a high variability of illumination conditions because of different weather conditions. This paper proposes a novel texture feature based robust expert system for vegetation identification. It consists of five steps, namely image pre-processing, feature extraction, training with multiple classifiers, classification, validation and statistical analysis. In the initial stage, Co-occurrence of Binary Pattern (CBP) technique is applied in order to obtain the texture feature relevant to vegetation in the roadside images. In the training and classification stages, three classifiers have been fused to combine the multiple decisions. The first classifier is based on Support Vector Machine, the second classifier is based on feed forward back-propagation neural network (FF-BPNN) and the third classifier is based on -Nearest Neighbor (k-NN). The proposed technique has been applied and evaluated on two types of vegetation images i.e. dense and sparse grasses. The classification accuracy with a success of 92.72% has been obtained using 5-fold cross validation approach. An (Analysis of Variance) test has also been conducted to show the statistical significance of results.

Process model comparison and similar processes retrieval are key issues to be addressed in many real world situations, and particularly relevant ones in some applications (e.g., in medicine), where similarity quantification can be exploited in a quality assessment perspective. Most of the process comparison techniques described in the literature suffer from two main limitations : (1) they adopt a purely syntactic (vs. semantic) approach in process activity comparison, and/or (2) they ignore complex control flow information (i.e., other than sequence). These limitations oversimplify the problem, and make the results of similarity-based process retrieval less reliable, especially when domain knowledge is available, and can be adopted to quantify activity or control flow construct differences. In this paper, we aim at overcoming both limitations , by introducing a framework which allows to extract the actual process model from the available process execution traces, through process mining techniques, and then to compare (mined) process models, by relying on a novel distance measure . The novel distance measure, which represents the main contribution of this paper, is able to address issues (1) and (2) above, since: (1) it provides a semantic, knowledge-intensive approach to process activity comparison, by making use of domain knowledge; (2) it explicitly takes into account complex control flow constructs (such as AND and XOR splits/joins), thus fully considering the different semantic meaning of control flow connections in a reliable way. The positive impact of the framework in practice has been tested in stroke management, where our approach has outperformed a state-of-the art literature metric on a real world event log, providing results that were closer to those of a human expert. Experiments in other domains are foreseen in the future.

Performance of three different fitness functions in credit scoring is evaluated.A novel fitness function based on subsets of client characteristics is proposed.The proposed fitness function shows better accuracy and sensitivity. Credit scoring methods have been widely investigated by researchers; recently, genetic algorithms have attracted particular attention. Many research papers comparing the performance of genetic algorithms and traditional scoring techniques have been published, but most do not provide enough detail about the fitness function used by the genetic algorithm-despite the fact that fitness function has a key influence on the model's overall performance. The aim of this paper is to evaluate the predictive performance of different fitness functions used by genetic algorithms in credit scoring. An alternative fitness function based on a variable bitmask is proposed, and its performance then compared with fitness functions based on a polynomial equation as well as an estimation of parameter range. The results suggest that the bitmask is superior to the two other methods in both accuracy and sensitivity. The Wilcoxon matched-pairs sign rank test and paired t-Test indicate these results are statistically significant.

Two stage fusion model comprising three machine learning techniques is used.Emphasis is on adequacy of information given to prediction models.First stage provides future value of statistical parameters helping the later stage.Two stage fusion model helps in decreasing overall prediction error. The paper focuses on the task of predicting future values of stock market index. Two indices namely CNX Nifty and S&P Bombay Stock Exchange (BSE) Sensex from Indian stock markets are selected for experimental evaluation. Experiments are based on 10years of historical data of these two indices. The predictions are made for 1-10, 15 and 30days in advance. The paper proposes two stage fusion approach involving Support Vector Regression (SVR) in the first stage. The second stage of the fusion approach uses Artificial Neural Network (ANN), Random Forest (RF) and SVR resulting into SVR-ANN, SVR-RF and SVR-SVR fusion prediction models. The prediction performance of these hybrid models is compared with the single stage scenarios where ANN, RF and SVR are used single-handedly. Ten technical indicators are selected as the inputs to each of the prediction models.

We have developed the system of gene selection from microarray data in autism composed of an ensemble of methods.We have developed the system of recognizing the autism applying the classifiers cooperating with gene selection.We have performed successful experiments of recognizing autism from reference cases using our system. The paper presents application of data mining methods for recognizing the most significant genes and gene sequences (treated as features) stored in a dataset of gene expression microarray. The investigations are performed for autism data. Few chosen methods of feature selection have been applied and their results integrated in the final outcome. In this way we find the contents of small set of the most important genes associated with autism. They have been applied in the classification procedure aimed on recognition of autism from reference group members. The results of numerical experiments concerning selection of the most important genes and classification of the cases on the basis of the selected genes will be discussed. The main contribution of the paper is in developing the fusion system of the results of many selection approaches into the final set, most closely associated with autism. We have also proposed special procedure of estimating the number of highest rank genes used in classification procedure.

In this paper we propose a new approach to identity verification based on the analysis of the dynamic signature. Considered problem seems to be particularly important in terms of biometrics. Effectiveness of signature verification significantly increases when dynamic characteristics of the signature are considered (e.g. velocity, pen pressure, etc.). These characteristics are individual for each user and difficult to forge. The effectiveness of the verification on the basis of an analysis of the dynamics of the signature can be further improved. A well-known way is to consider the characteristics of the signature in the sections called partitions. In this paper we propose a new method for identity verification which uses partitioning. Partitions represent time moments of signing of the user. In the classification process the partitions, in which the user created more stable reference signatures during acquisition phase, are more important. Other important features of our method are: using capabilities of fuzzy set theory and development on the basis of them the flexible neuro-fuzzy systems and interpretable classification system for final signature classification. In this paper we have included the simulation results for the two currently available databases of dynamic signatures: free SVC2004 and commercial BioSecure database.

We provide detailed analysis of gray-sheep users problem in recommender systems.We show how conventional collaborative filtering fail for gray-sheep users problem.We use K-means clustering to separate these users from rest of the users.We propose switching hybrid recommender system to overcome this problem. Recommender systems apply data mining and machine learning techniques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on gray-sheep users problem responsible for the increased error rate in collaborative filtering based recommender systems. This paper makes the following contributions: we show that (1) the presence of gray-sheep users can affect the performance - accuracy and coverage - of the collaborative filtering based algorithms, depending on the data sparsity and distribution; (2) gray-sheep users can be identified using clustering algorithms in offline fashion, where the similarity threshold to isolate these users from the rest of community can be found empirically. We propose various improved centroid selection approaches and distance measures for the K-means clustering algorithm; (3) content-based profile of gray-sheep users can be used for making accurate recommendations. We offer a hybrid recommendation algorithm to make reliable recommendations for gray-sheep users. To the best of our knowledge, this is the first attempt to propose a formal solution for gray-sheep users problem. By extensive experimental results on two different datasets (MovieLens and community of movie fans in the FilmTrust website), we showed that the proposed approach reduces the recommendation error rate for the gray-sheep users while maintaining reasonable computational performance.

This study proposes new method to overcome sparsity problem of document clustering.We build combined method using dimension reduction, K-means clustering, and SVC.In particular, we attempt to overcome the sparseness in patent document clustering.First, we conduct experiment using news data from UCI machine learning repository.Second, using retrieved patent documents, we carry out patent clustering. Many studies on developing technologies have been published as articles, papers, or patents. We use and analyze these documents to find scientific and technological trends. In this paper, we consider document clustering as a method of document data analysis. In general, we have trouble analyzing documents directly because document data are not suitable for statistical and machine learning methods of analysis. Therefore, we have to transform document data into structured data for analytical purposes. For this process, we use text mining techniques. The structured data are very sparse, and hence, it is difficult to analyze them. This study proposes a new method to overcome the sparsity problem of document clustering. We build a combined clustering method using dimension reduction and K-means clustering based on support vector clustering and Silhouette measure. In particular, we attempt to overcome the sparseness in patent document clustering. To verify the efficacy of our work, we first conduct an experiment using news data from the machine learning repository of the University of California at Irvine. Second, using patent documents retrieved from the United States Patent and Trademark Office, we carry out patent clustering for technology forecasting.

Stress is a major health problem in our world today. For this reason, it is important to gain an objective understanding of how average individuals respond to real-life events they observe in environments they encounter. The aims of this paper are to introduce the concept of observer stress and investigate whether a computational model can be developed to recognize observer stress using physiological and physical response sensor signals. The paper discusses the motivations for the investigation and details the experiments for data collection for observers of real-life settings which used unobtrusive methods suited to real-life environments. It describes an individual-independent support vector machine based model classifier to recognize stress patterns from observer response signals. A genetic algorithm is used for feature selection to build a classifier. The classifier recognized observer stress with an accuracy of 98%. The outcomes of this research provide a new application area for knowledge discovery and data mining to predict human stress response to real-life environments and a possible future extension on managing stress objectively.

This paper focuses on high-dimensional and ultrahigh dimensional multi-category problems and presents a feature selection framework based on local recursive feature elimination (Local-RFE). Using this analytical framework, we propose a new feature selection algorithm, PLS-based local-RFE (LRFE-PLS). In order to compare the effectiveness of the proposed methodology, we also present PLS-based Global-RFE which takes all categories into consideration simultaneously. The advantage of the proposed algorithms lies in the fact that PLS-based feature ranking can quickly delete irrelevant features and RFE can concurrently remove some redundant features. As a result, the selected feature subset is more compact. In this paper the proposed algorithms are compared to some state-of-the-art methods using multiple datasets. Experimental results show that the proposed algorithms are competitive and work effectively for high-dimensional multi-category data. Statistical tests of significance show that LRFE-PLS algorithm has better performance. The proposed algorithms can be effectively applied not only to microarray data analysis but also to image recognition and financial data analysis.

Intelligent metering technology combined with advanced numerical techniques enable a paradigm shift in the current level of water consumption information provision that is available to the customer and the water business. The aim of this study was to develop an autonomous and intelligent system for residential water end-use classification that could interface with customers and water business managers via a user-friendly web-based application. Water flow data collected directly from smart water meters includes both single (e.g., a shower event occurring alone) and combined (i.e., an event that comprises several overlapping single events) water end use events. The authors recently developed intelligent algorithms to solve the complex problem of autonomously categorising residential water consumption data into a registry of single and combined events using a hybrid combination of techniques including Hidden Markov Model (HMM), Dynamic Time Warping (DTW) algorithm, time-of-day probability functions, threshold values and various physical features. However, the issue still remained, which is the focus of this current paper, on how to integrate self-learning functionality into the visioned expert system, in order that it can learn from newly collected datasets from different cities, regions and countries, to that collected for the training data. Such versatility and adaptive capacity is essential to make the expert system widely applicable. Through applying alternate forms of HMM and DTW in association with a frequency analysis technique, a suitable self-learning methodology was formulated and tested on three independent households located in Melbourne, Australia with a prediction accuracy of between 80% and 90% for the major end-use categories. The three principle flow data processing modules (i.e., single and combined event recognition and self-learning function) were integrated into a prototype software application for performing autonomous water end-use analysis and its functionality is presented in the latter sections of this paper. The developed expert system has profound implications for government, water businesses and consumers, seeking to better manage precious urban water resources.

Low back disorders (LBDs) due to manual material lifting tasks have become a significant issue which affects the quality of life of industrial population of workers in the U.S. and has enormous economic impact. For the last three decades researchers have been trying to understand the phenomena of LBDs and develop practical guidelines which could prevent these injuries from happening or limit the severity of these injuries after they have already occurred. One of the research streams concentrated on creating and testing various classification models based on a landmark Marras data set. The goal of these models was to categorize manual lifting jobs as low risk or high risk with respect to LBDs. This paper summarizes and critiques the previous approaches as some of them yielded unrealistically high classification accuracy rates. The paper also proposes an adaptive neuro-fuzzy inference system (ANFIS) to classify tasks into high risk or low risk. To our best knowledge ANFIS has not been used in this context yet and has not been used for classification of a binary target variable. The paper also compares the classification performances of the different parameters or configurations of ANFIS. The ANFIS model appears to be a viable option for risk classification as it exhibits the classification accuracy rates consistent with several previous studies. More importantly ANFIS generates easy to interpret control surfaces, membership functions, and fuzzy rules, thus allowing one to get a deeper insight into the relationships between risk factors which interact with each other in a complex and nonlinear way. Such insights could prove to be very useful for the much needed efforts to better understand LBDs.

The paper presents an automatic approach to the recognition of the industrial tools on the basis of their image registered by the camera. The solved tasks include: the automatic localization of the tools in the image, preprocessing of the image (binarization, noise removal, filling the holes, normalization, etc.), generation of the numerical descriptors, verification of descriptors in the role of the diagnostic features, selection of the features and final stage of classification of the tools. The efficiency of the developed system has been verified on the example of exemplary set of industrial tools and the results of this verification are presented and discussed in the paper.

This paper studies the reliability of geometric features for the identification of users based on hand biometrics. Our methodology is based on genetic algorithms and mutual information. The aim is to provide a system for user identification rather than a classification. Additionally, a robust hand segmentation method to extract the hand silhouette and a set of geometric features in hard and complex environments is described. This paper focuses on studying how important and discriminating the hand geometric features are, and if they are suitable in developing a robust and reliable biometric identification. Several public databases have been used to test our method. As a result, the number of required features have been drastically reduced from datasets with more than 400 features. In fact, good classification rates with about 50 features on average are achieved, with a 100% accuracy using the GA-LDA strategy for the GPDS database and 97% for the CASIA and IITD databases, approximately. For these last contact-less databases, reasonable EER rates are also obtained.

Patent intelligence-the transformation of content found in multiple patents into technical, business, and legal insight-is considered a key factor in gaining a competitive advantage in technologically competitive business environments. Although keyword-based patent intelligence tools are widely used due to their simplicity and ease of use, they are limited in that they cannot represent key technological concepts and inventive knowledge by relying only on the frequency of occurrence of defined keywords. As a remedy, this paper proposes a Subject-Action-Object (SAO)-based patent intelligence system. SAO structures that can be extracted from textual patent information are known as the expertise and inventive findings of the relevant patent. On the basis of semantic analysis of patent SAO structures, our proposed intelligence system constructs patent maps and patent networks. Building on the maps and networks, the system provides specific functionalities including identification of technology trends and significant patents, detection of novel technologies, and identification of potential infringement. This paper describes the architecture of our proposed patent intelligence system in detail, and illustrates the system's functionalities using case studies. We anticipate that our proposed system will be incorporated into the technology planning process to assist experts in the formulation of technology strategies.

Knowledge management (KM) and data mining (DM) have become more important today, however, there are few comprehensive researches and categorization schemes to discuss the characteristics for both of them. Using a bibliometric approach, this paper analyzes KM and DM research trends, forecasts and citations from 1989 to 2009 by locating headings ''knowledge management'' and ''data mining'' in topics in the SSCI database. The bibliometric analytical technique was used to examine these two topics in SSCI journals from 1989 to 2009, we found 1393 articles with KM and 1181 articles with DM. This paper implemented and classified KM and DM articles using the following eight categories-publication year, citation, country/territory, document type, institute name, language, source title and subject area-for different distribution status in order to explore the differences and how KM and DM technologies have developed in this period and to analyze KM and DM technology tendencies under the above result. Also, the paper performs the K-S test to check whether the distribution of author article production follows Lotka's law. The research findings can be extended to investigate author productivity by analyzing variables such as chronological and academic age, number and frequency of previous publications, access to research grants, job status, etc. In such a way characteristics of high, medium and low publishing activity of authors can be identified. Besides, these findings will also help to judge scientific research trends and understand the scale of development of research in KM and DM through comparing the increases of the article author. Based on the above information, governments and enterprises may infer collective tendencies and demands for scientific researcher in KM and DM to formulate appropriate training strategies and policies in the future. This analysis provides a roadmap for future research, abstracts technology trend information and facilitates knowledge accumulations, therefore the future research can concentrated in core categories. This implies that the phenomenon ''success breeds success'' is more common in higher quality publications.

Highlights? We design an Expert System for plant discrimination in agricultural fields. ? A decision making module determines the image quality. ? A greenness identification module extracts green plants. ? The Expert System is valid under adverse conditions and different devices. ? Plants segmentation is required for weeds specific treatments. It is well-known that one important issue emerging strongly in agriculture is related with the automation of tasks, where camera-based sensors play an important role. They provide images that must be conveniently processed. The most relevant image processing procedures require the identification of green plants, in our experiments they comes from barley and maize fields including weeds, so that some type of action can be carried out, including site-specific treatments with chemical products or mechanical manipulations.The images come from outdoor environments, which are affected for a high variability of illumination conditions because of sunny or cloudy days or both with high rate of changes.Several indices have been proposed in the literature for greenness identification, but under adverse environmental conditions most of them fail or do not work properly. This is true even for camera devices with auto-image white balance.This paper proposes a new automatic and robust Expert System for greenness identification. It consists of two main modules: (1) decision making, based on image histogram analysis and (2) greenness identification, where two different strategies are proposed, the first based on classical greenness identification methods and the second inspired on the Fuzzy Clustering approach. The Expert System design as a whole makes a contribution, but the Fuzzy Clustering strategy makes the main finding of this paper. The system is tested for different images captured with several camera devices.

This paper investigates a number of computational intelligence techniques in the detection of heart disease. Particularly, comparison of six well known classifiers for the well used Cleveland data is performed. Further, this paper highlights the potential of an expert judgment based (i.e., medical knowledge driven) feature selection process (termed as MFS), and compare against the generally employed computational intelligence based feature selection mechanism. Also, this article recognizes that the publicly available Cleveland data becomes imbalanced when considering binary classification. Performance of classifiers, and also the potential of MFS are investigated considering this imbalanced data issue. The experimental results demonstrate that the use of MFS noticeably improved the performance, especially in terms of accuracy, for most of the classifiers considered and for majority of the datasets (generated by converting the Cleveland dataset for binary classification). MFS combined with the computerized feature selection process (CFS) has also been investigated and showed encouraging results particularly for NaiveBayes, IBK and SMO. In summary, the medical knowledge based feature selection method has shown promise for use in heart disease diagnostics.

The assessment of promotional sales with models constructed by machine learning techniques is arousing interest due, among other reasons, to the current economic situation leading to a more complex environment of simultaneous and concurrent promotional activities. An operative model diagnosis procedure was previously proposed in the companion paper, which can be readily used both for agile decision making on the architecture and implementation details of the machine learning algorithms, and for differential benchmarking among models. In this paper, a detailed example of model analysis is presented for two representative databases with different promotional behaviour, namely, a non-seasonal category (milk) and a heavily seasonal category (beer). The performance of four well-known machine learning techniques with increasing complexity is analyzed in detail here. In particular, k-Nearest Neighbours, General Regression Neural Networks, Multilayer Perceptron (MLP), and Support Vector Machines (SVM), are differentially compared. Present paper evaluates these techniques along the experiments described for both categories when applying the methodological findings obtained in the companion paper. We conclude that some elements included in the architecture are not essential for a good performance of the machine learning promotional models, such as the semiparametric nature of the kernel in SVM models, whereas other can be strongly dependent of the database, such as the convenience of multiple output models in MLP regression schemes. Additionally, the specificity of the behaviour of certain categories and product ranges determines the need to establish suitable and specific procedures for a better prediction and feature extraction.