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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.

Low quality of business process event logs, as determined by anomalous and missing values, is often unavoidable in practical contexts. The output of process analysis that uses event logs with missing and anomalous values is also likely to be of low quality, thus decreasing the quality of any decisions based on it. While previous work has focused on reconstructing missing events in an event log or removing anomalous traces, in this paper we focus on detecting anomalous values and reconstructing missing values at the level of attributes in event logs. We propose methods based on autoencoders, which are a class of neural networks that can reconstruct their own input and are particularly suitable to learn a model of the complex relationships among attribute values in an event log. These methods do not rely on any a-priori knowledge about the business process that generated an event log and are evaluated using real world and artificially-generated event logs. The paper also discusses a qualitative analysis of the impact of event log cleaning and reconstruction on the output of process discovery. The proposed approach shows remarkable performance regarding activity labels and timestamps in artificial event logs. The performance in the case of real world event logs, in particular timestamp anomaly detection, is lower, which may be due to high variability of attribute values in the chosen event logs. Process models discovered from reconstructed event logs are characterised by lower variability of allowed behaviour and, therefore, are more usable in practice.

Machine learning has become a pillar of today’s expert and intelligent systems where a special attention has been drawn to unsupervised methods. Support vector data description is one of the most interesting methods proposed in this context that addresses two main challenges: the absence of ground truth labels and the presence of outliers in data. It is well known that the ultimate problem has a great impact on a system performance since it typically prevents and limits its generalization and hence its failure. Even though the strengths of the Support vector data description method, its application into multi class and large scale context, as computer vision systems, raises bottleneck issues due to its runtime complexity and mono-class context application. We propose, in this paper, an approach inspired from the movement behavior of particle swarm and relies on the use of core set paradigm to overcome the aforementioned drawbacks of Support vector data description method. Indeed, since the proposed method uses some parameters corresponding to kernel function and swarm’s movement formulation, we present and study in this paper some strategies to ensure a dynamic adaptation of these parameters. The main qualities of the proposed method is that it provides a fast scheme for clustering a set of points without neither a prior knowledge on the number of naturally occurring cluster in the data nor an assumption on clusters shapes. A quantitative and comparative performance assessment is carried out over the Berkeley and BuffaloXiph datasets. The results witness the efficiency of the method in terms of both classification accuracy and computational performance. These quantitative assessments reinforce the significance as well as the importance of embedding the proposed method in other intelligent systems application areas.

The present paper aims to show the necessity of information augmentation to cope with the natural decrease in information content in multi-layered neural networks. It is natural to make an effort to collect as much information as possible, because it is impossible to know which information is necessary or useful before learning. Thus, the present paper tries to force neural networks to form new network configurations to have as much information as possible, contrary to the conventional approach of information reduction, such as many types of regularization. For information augmentation, we use the self-organizing map (SOM), which can over-represent inputs and produce as many similar weights as possible. The method was applied to two data sets: the banknote authentication data set and the character recognition data set. In both experimental results, it was confirmed that redundant and excessive information generation in terms of the excessive number of connection weights was connected with improved generalization.

Clustering of multi-view data has received increasing attention since it explores multiple views of data sets aiming at improving clustering accuracy. Particle Swarm Optimization (PSO) is a well-known population-based meta-heuristic successfully used in cluster analysis. This paper introduces two hybrid clustering methods for multi-view relational data. These hybrid methods combine PSO and hard clustering algorithms based on multiple dissimilarity matrices. These methods take advantage of the global convergence ability of PSO and the local exploitation of hard clustering algorithms in the position update step, aiming to improve the balance between exploitation and exploration processes. Moreover, the paper provides adapted versions of 11 fitness functions suitable for vector data aiming at dealing with multi-view relational data. Two performance criteria were used to evaluate the clustering quality using the two proposed methods over eleven real-world data sets including image and document data sets. Among new findings, it was observed that the top three fitness functions are Silhouette index, Xu index and Intra-cluster homogeneity. The performance of the proposed algorithms was compared with previous single and multi-view relational clustering algorithms. The results show that the proposed methods significantly outperformed the other algorithms in the majority of cases. The results reinforce the importance of the application of techniques such as PSO-based clustering algorithms in the field of expert systems and machine learning. Such application enhances classification accuracy and cluster compactness. Besides, the proposed algorithms can be useful tools in content-based image retrieval systems, providing good categorizations and automatically learning relevance weights for each cluster of images and sets of views.

This paper presents an application of a recently well performing evolutionary algorithm for continuous numerical optimization, Success-History Based Adaptive Differential Evolution Algorithm (SHADE) including Linear population size reduction (L-SHADE), to an expert system for underwater glider path planning (UGPP). The proposed algorithm is compared to other similar algorithms and also to results from literature. The motivation of this work is to provide an alternative to the current glider mission control systems, that are based mostly on multidisciplinary human-expert teams from robotic and oceanographic areas. Initially configured as a decision-support expert system, the natural evolution of the tool is targeting higher autonomy levels. To assess the performance of the applied optimizers, the test functions for UGPP are utilized as defined in literature, which simulate real-life oceanic mission scenarios. Based on these test functions, in this paper, the performance of the proposed application of L-SHADE to UGPP is aggregated using statistical analyis. The depicted fitness convergence graphs, final obtained fitness plots, trajectories drawn, and per-scenario analysis show that the new proposed algorithm yields stable and competitive output trajectories. Over the set of benchmark missions, the newly obtained results with a configured L-SHADE outperforms existing literature results in UGPP and ranks best over the compared algorithms. Moreover, some additional previously applied algorithms have been reconfigured to yield improved performance. Thereby, this new application of evolutionary algorithms to UGPP contributes significantly to the capacity of the decision-makers, when they use the improved UGPP expert system yielding better trajectories.

In knowledge discovery studies, association rules mining has been extensively studied to discover hidden knowledge and relationships among set of items in a transactional dataset. Most research on association rule mining focuses on discovering frequent patterns based on the most frequent items occurring in the dataset. However, the process of extracting rare rules has received less attention. In medical dataset studies, the discovery of rare association rules (RARs) is more challenging, because it could likely be used to obtain more potentially rare and unusual knowledge for physicians, beside frequent association rules. Hence, the aim of this paper is to discover non-frequent or rare-unusual association rules (RUARs) from a stroke medical dataset to provide potential meaningful knowledge to the user domain. A discretization method needs to be performed as the data preprocessing step before generating rules. To the best of our knowledge, fewer studies have focused on the role of discretization results to support the extraction of a better amount and quality of RUARs, particularly for medical datasets. In addition, the extracted RUARs is expected to provide potential new unusual insights on stroke risk patterns. This paper applies mutual information measure to discretize a stroke examination dataset collected from a medical center in Taiwan. The interval merging method was proposed to simplify the discrete form and enrich the quality of generated rules. Towards the end, rare association rules, with relatively low support, were generated by employing the Apriori-Rare method accordingly. In addition, a filtering process was applied to the content of the rule itemsets to discover the expected set of RUARs for physicians. Furthermore, the extracted RUARs was analyzed based on the relative risk values toward the occurrence of stroke. Results indicated that the mutual information discretization outperformed the traditional discretization methods in terms of how the discretization scheme can support the extraction of RUARs with a better quantity and quality measurements for further analysis purpose in medical point of view. Moreover, the proposed method had a relatively higher number of RUARs. The knowledge of unusual rule patterns from rare association rules might provide potential new and unusual insights for medical pratitioners and increase the awareness of stroke examination results.

As an important tool for information fusion, evidence theory has been widely applied in many areas. Unlike spatial evidence combination, temporal evidence combination is sequential and dynamic, which calls for new temporal evidence combination rule. In this paper, temporal evidence is combined based on relative reliability evaluation and evidence discounting. We first recall the method of evidence reliability evaluation based on intuitionistic fuzzy multiple criteria decision making (ERE-IFMCDM). Based on ERE-IFMCDM method, relative reliability factors of evidence sources in neighboring time nodes can be obtained. Then, according to evidence discounting and Dempster's combination rule, a method for temporal evidence combination based on the relative reliability factor is developed. Numerical examples and simulation demonstrate that the proposed method is time sensitive, which can reflect the dynamic feature of temporal information fusion. Moreover, the proposed method can deal with conflict in temporal fusion much better, which can enhance the anti-interference performance of the fusion system. The fusion framework proposed in this paper provides a dynamic perspective of the fusion of temporal uncertain information. It is helpful for the structural design of intelligent fusion system based on multiple temporal-spatial information.

The learning algorithm for multivariable data analysis (LAMDA) is a learning method to group or classify quantitative and qualitative historical data. LAMDA can be applied for self-learning and directed learning. Considering self-learning, LAMDA groups historical data through the global membership degrees of a sample with respect to a class, considering the contribution of all features for each sample. Such contribution is called marginal adequacy degree (MAD). To mix all MADs, several aggregation functions such as linear and geometrical connectives are used. The aggregation function calculates the global adequacy degree (GAD) of a sample with respect to a class. This procedure avoids assigning the class number as initial parameter due to that LAMDA applies an automatic threshold called non-informative class (NIC) which finds the best assignment of a portion of data to one class. One improvement for the LAMDA algorithm is the use of a full reinforcement operator called Yager–Rybalov Triple Pi, which can reduce the number of classes. However, this kind of solution ignores the degree to which one sample belongs to one class relative to all samples. Another limitation is that the classes reduction is not controlled by a parameter or an algorithm, which removes some classes required by a human expert. Such removed classes can be useful for the human expert in the data analysis of complex processes or for intelligent systems when the searching of additional information is required in decision-making. In this paper, the typicality degree and the intuitionistic fuzzy sets (IFSs) are used to propose one improvement of the LAMDA method in order to develop a strategy that avoids the loss of significant classes. This paper shows two functions: global typicality degree (GTD) and intuitionistic global adequacy degree (IGAD). By integrating both functions, a new function called typicality and intuitionistic global adequacy degree (TIGAD) is proposed. The proposal is applied in three study cases. The results show an improvement of clustering which were validated by the human expert and the information of data. Furthermore, the impact of the research is focused on data analysis, where the expert should make the best decision.

International audience; Prognostics and health management can improve the reliability and safety of transportation systems. Data collected from diverse sources provide a chance and at the same time a challenge for data-driven PHM methods and models. The data often exhibit challenging characteristics like imbalanced data on normal and faulty conditions, noise and outliers, data points of different importance for the data-driven model, etc. In this paper, a k nearest neighbors-based fuzzy support vector machine is proposed for reducing the computational burden and tackling the issue of imbalance and outlier data, in fault detection. Fault detection is mathematically a classification problem. In this paper, the reverse nearest neighbors technique is adopted for detecting outliers and the k nearest neighbors technique is used to identify the borderline points for defining the classification hyperplane in support vector machines. Considering the position of each data point and the distribution of its nearest neighbors, a new method is proposed for calculating their estimation error costs. The proposed method is verified by comparison with several benchmark methods on five public datasets. Then, a real case study concerning fault detection in a braking system of a high-speed train is considered.

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.

In this paper, we propose a Deep Belief Network (DBN) based approach for the classification of audio signals to improve work activity identification and remote surveillance of construction projects. The aim of the work is to obtain an accurate and flexible tool for consistently executing and managing the unmanned monitoring of construction sites by using distributed acoustic sensors. In this paper, ten classes of multiple construction equipment and tools, frequently and broadly used in construction sites, have been collected and examined to conduct and validate the proposed approach. The input provided to the DBN consists in the concatenation of several statistics evaluated by a set of spectral features, like MFCCs and mel-scaled spectrogram. The proposed architecture, along with the preprocessing and the feature extraction steps, has been described in details while the effectiveness of the proposed idea has been demonstrated by some numerical results, evaluated by using real-world recordings. The final overall accuracy on the test set is up to 98% and is a significantly improved performance compared to other state-of-the-are approaches. A practical and real-time application of the presented method has been also proposed in order to apply the classification scheme to sound data recorded in different environmental scenarios.

This paper investigates the link prediction in multiplex networks. Multiplex networks that represent multiple types of interaction between the same group of individuals are a special case of complex networks. Each type of interaction is modeled as a layer in a multiplex network. Usually, the topological structures between different layers of a multiplex network have a certain extent of correlation. As a result, the accuracy of link prediction in multiplex networks can be enhanced by combining the information of different layers. In this paper, link prediction in multiplex networks is regarded as a multiple-attribute decision-making problem, in which the potential links in the target layer are considered as alternatives, layers are viewed as attributes, and the similarity score of a potential link in each layer is an attribute value. In implementation, the TOPSIS method is employed to rank alternatives, and interlayer relevance is used to weight the attributes. The experimental results show that the proposed method is not sensitive to the parameter and the interlayer relevance measure, and achieves superior prediction accuracy.

Semantic web technology seems to be in the infant stage as only little efforts have been taken on ontology construction with cross-domain application. This paper intends to take an effort on a new workspace, in which the ontology construction model under cross-domain application is performed. The core concern of this work is on two decision-making process namely data filtering and data annotation. Certain process is followed in this work: (i) Preprocessing (ii) Proposed Jaccard Similarity Evaluation (iii) Data filtering and Outlier Detection (iv) Semantic annotation and clustering. More particularly, data filtering is performed based on the evaluated similarity function. The outliers are identified and grouped separately. The data annotation is performed based on the semantics and thereby the clustering process takes place to form the ontology precisely. This clustering process obviously relies to the optimization crisis as the optimal centroid selection becomes the greatest issue. In order to solve this, this paper extends with the introduction of a hybrid algorithm named Circling Insisted-Rider Optimization Algorithm (CI-ROA), which hybrids the concept of Whale Optimization Algorithm (WOA) and Rider Optimization Algorithm (ROA), respectively. Finally, the performance of proposed work is compared and proved over other state-of-the-art models.

In recent years, the advancement in technology and the availability of a large number of sensors has led to a huge amount of real-time streaming data. The real-time data measured by Phasor Measurement Units (PMU), are of streaming data type and their analysis can identify the status of the power system failure which leads to system instability. Hence online monitoring of PMU data is extremely important which includes preprocessing, filtering, and analysis before taking any corrective actions in the power system. One of the crucial analyses that can be performed over PMU data is the identification of anomalies that arise due to several factors. Early detection of anomalies in the PMU streaming data is highly useful though in real-time it is difficult to detect anomalies consistently. This paper proposes a novel framework for detecting anomalies in multivariate streaming PMU data based on the Density Estimation technique. The main novelty of this paper is that the proposed model is suitable for any kind of anomaly detection in multivariate streaming PMU data which is not addressed in literatures. The proposed system implements Principal Component Analysis in streaming real-time data to select the relevant features. The K-Means algorithm is performed over the selected feature vectors to find the number of clusters in the streaming PMU data. The Gaussian Mixture Model (GMM) is developed to represent the normal PMU data of the system. The minimum and maximum value of the probability of the training samples being generated by the model is selected as a minimum and maximum threshold. The samples with probability less than the minimum threshold or greater than the maximum threshold are considered as anomalies. The main advantage of this proposed model is that it can perform anomaly detection in multivariate streaming PMU data even online within the specified window. The proposed system can identify real time anomalies of multivariate dataset even by offline training model of GMM. The proposed model is developed in MATLAB 2018b version and is tested in streaming PMU data both for offline and online datasets. The performance of the proposed model is validated using performance metrics and the results show that F1 score is 0.95 for 2 features, 0.91 for 3 features, and 0.99 for 8, 12 and 16 features and 1 for 10 features in offline mode. The performance evaluation of testing data in online mode for 2, 3, 8, 10, 12 and 16 features are obtained which proves that the system is adapting to the streaming PMU data with reduced false positive rate and F1 score of 1 for 8, 10, 12 and 16 features. The experimental results prove that the proposed framework for anomaly detection in streaming data is more effective and accurate in identifying the anomalies in real time PMU streamed dataset.

Current software systems are inherently complex and this fact strongly complicates, and makes more expensive, to validate them. Therefore, it is a must to provide methodologies, supported by tools, that can direct validation activities so that they focus on specific aspects of the system (e.g. its critical parts, common errors produced by developers, components that are expensive to fix after deployment, etc). Among the different validation techniques, testing is the most widely used. In this paper we focus on one of the main problems when testing systems with many components: the likelihood of Failed Error Propagation (FEP). FEP appears when we have faulty components such that their wrong behaviour is not revealed when isolatedly testing them but that might produce an error when they are combined with other components. Given a component, it is not possible to automatically assess the likelihood of FEP. However, previous work has shown that there is a strong correlation between the likelihood of FEP and an Information Theory notion called Squeeziness . Recent work has shown that it is possible to compute different values of Squeeziness (essentially, Squeeziness depends on a positive real value) and some of them are more suitable to estimate FEP. In this paper we present our tool SqSelect . Our tool receives either a specific system or its more important characteristics (number of states, maximum and minimum number of outgoing transitions from a state, size of the input and output alphabets) and returns interesting data that can help the tester to estimate the presence of FEP. In particular, our tool provides the most promising value(s) of the parameter associated with Squeeziness so that the likelihood of FEP can be more accurately estimated. In order to compute these values, our tool relies on an artificial neural network that has been extensively trained (compressing the information from around 250 , 000 systems and around 1 , 500 , 000 executions).

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.

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.

This paper introduces a novel proposal to discover the best associative classification rules through studying the influence of the attributes used in robust catalogues. Notice that a catalogue is defined as a dataset free of duplicate records. Moreover, a robust catalogue is obtained when incomplete records and those with uncertainty are eliminated from a catalogue. Therefore, a robust catalogue is a collection of association rules with 100% confidence and unitary support. In this paper we demonstrate that robust catalogues contain the same association rules as the datasets from which they were obtained, but can be managed in memory without eliminating any data from the analysis. In fact, the experiments performed show that all robust catalogues contained in a classification dataset are easily obtained, providing millions of associative classification rules with 100% confidence to the expert researcher in data mining.

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.

Speaker Identification (SI) is the process of identifying the speaker from a given utterance by comparing the voice biometrics of the utterance with those utterance models stored beforehand. SI technologies are taken a new direction due to the advances in artificial intelligence and have been used widely in various domains. Feature extraction is one of the most important aspects of SI, which significantly influences the SI process and performance. This systematic review is conducted to identify, compare, and analyze various feature extraction approaches, methods, and algorithms of SI to provide a reference on feature extraction approaches for SI applications and future studies. The review was conducted according to Kitchenham systematic review methodology and guidelines, and provides an in-depth analysis on proposals and implementations of SI feature extraction methods discussed in the literature between year 2011 and 2106. Three research questions were determined and an initial set of 535 publications were identified to answer the questions. After applying exclusion criteria 160 related publications were shortlisted and reviewed in this paper; these papers were considered to answer the research questions. Results indicate that pure Mel-Frequency Cepstral Coefficients (MFCCs) based feature extraction approaches have been used more than any other approach. Furthermore, other MFCC variations, such as MFCC fusion and cleansing approaches, are proven to be very popular as well. This study identified that the current SI research trend is to develop a robust universal SI framework to address the important problems of SI such as adaptability, complexity, multi-lingual recognition, and noise robustness. The results presented in this research are based on past publications, citations, and number of implementations with citations being most relevant. This paper also presents the general process of SI.

This paper presents a robust system for the document layout analysis.The proposed system is based on multilevel homogeneity structure (MHS).The proposed system is designed to work with many different document languages.Our system is tested on four published datasets with different document languages.The proposed system (MHS) won the RDCL-2015 competition (ICDAR2015). One of the difficulties in the understanding of document images is document layout analysis, which is the first step in document image modeling. In this paper, a robust system for which a multilevel-homogeneity structure is used in accordance with a hybrid methodology is proposed to deal with this problem. Our system consists of the following three main stages: classification, segmentation, and refinement and labeling. Different from other page segmentation methods, the proposed system includes an efficient algorithm to detect table regions in document images. Besides, to create an effective application, the proposed system is designed to work with a variety of document languages. The proposed method was tested with the ICDAR2015 competition (RDCL-2015) and three other published datasets in different languages. The results of these tests show that the accuracy of proposed system is superior to the previous methods.

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.

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.

An anomaly detection algorithm to identify AIS on-off switching is proposed.The algorithm exploits the AIS message Received Signal Strength Indicator.Machine Learning algorithms are used to build normality models.AIS reception is characterized by using real word data.The methodology is scalable from one station to a network of receivers. The Automatic Identification System (AIS) is a ship reporting system based on messages broadcast by vessels carrying an AIS transponder. The recent increase of terrestrial networks and satellite constellations of receivers is making AIS one of the main sources of information for Maritime Situational Awareness activities. Nevertheless, AIS is subject to reliability and manipulation issues; indeed, the received reports can be unintentionally incorrect, jammed or deliberately spoofed. Moreover, the system can be switched off to cover illicit operations, causing the interruption of AIS reception. This paper addresses the problem of detecting whether a shortage of AIS messages represents an alerting situation or not, by exploiting the Received Signal Strength Indicator available at the AIS Base Stations (BS). In designing such an anomaly detector, the electromagnetic propagation conditions that characterize the channel between ship AIS transponders and BS have to be taken into consideration. The first part of this work is thus focused on the experimental investigation and characterisation of coverage patterns extracted from the real historical AIS data. In addition, the paper proposes an anomaly detection algorithm to identify intentional AIS on-off switching. The presented methodology is then illustrated and assessed on a real-world dataset.

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.

The paper considers a problem of forecasting of news feeds content. Analysis of existing approaches to this problem solution reveals the need for development of methods with enhanced forecasting capabilities. A method is proposed with expanded accounting for space and time relationships of the processed data. The method is revealed through an example of a neural network forecasting system that implements it. Implementation includes data retrieval from news feeds, their special preprocessing, coding and forecasting of words sets and their interconnections, followed by highlighting news topics and describing the of news feeds content. Some variants of stream recurrent neural networks with spiral layer structures were investigated with due regard to their forecasting capabilities under direction and strength control of the associative call of signals from the network memory. The paper also presents and discusses experimental results, a description of the methodological contribution and recommendations on the method practical application.

This paper presents a small-sample faulty line detection method for a small current grounded system (SCGS) using improved generative adversarial networks (GANs) to overcome the problems of low accuracy and poor economy associated with traditional methods. First, we collected real-time fault data of the dispatching system as the original sample and used the linear interpolation and normalization methods to preprocess the data. We extracted and labeled the features of the original sample to construct a feature vector that represents the single-phase grounded fault. Then, to solve the problem of insufficient faulty samples, this paper used the GAN to generate synthetic samples to expand the training sample size. To stabilize the GAN training, the Wasserstein distance was introduced to improve the GAN loss function (WGAN). Finally, for the synthetic samples, we used Batch Normalization (BN) and an Early Stopping algorithm to train the lightweight depthwise separable convolutional network (DSCN) classifier and obtain a faulty line detection model with fast convergence and high accuracy. With the data of an actual dispatching system in China as an example, the proposed method determined the faulty line within 100–200 ms with an accuracy of more than 96%, which is approximately 26% higher than the accuracy of traditional methods. Compared with mainstream machine learning algorithms, the proposed method realizes the self-extraction of faulty features and simplifies the algorithm. Moreover, the accuracy of the proposed method is much higher than that of other machine learning algorithms on small samples. It is worth noting that this method needs only the existing real-time dispatching data and does not need to install additional data acquisition and fault detection equipment, eliminating the complicated equipment installation and debugging process and greatly reducing equipment costs human operations and maintenance costs. The proposed method has been implemented in an actual power system in China for two years. The results show that it is highly accurate and cost-effective, performs well in real time, and has bright prospects for future applications.

In some applications, a relation between a response variable and one or more explanatory variables (referred as a “profile”) characterizes the quality of a process. Profile monitoring is commonly performed through statistical methods, while machine learning schemes have not received much attention in this regard. In this paper, a control chart based on Artificial Neural Networks (ANN) is proposed to monitor linear profiles in phase II. In the proposed control chart, some novel run rules as the major contribution of this paper are also used to enhance the efficiency of the control chart and for faster detection of shifts. Simulation results revealed a good performance of the proposed control chart based on average run length (ARL) criterion. Further, a systematic ANN-based diagnostic procedure was proposed to identify which parameter has changed in the process. Finally, the implementation of the proposed scheme was illustrated through a real calibration example from the field of chemical engineering.

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.

SAM provides enhanced visual aids for representing the many-to-many association rules in large transaction data.The performance of SAM can be numerically evaluated by using S2C measure.SAM enables users to conveniently identify the interesting areas that might contain interesting association rules.SAMs with higher S2C values are more useful for visual exploration of association rules. The association rule mining is one of the most popular data mining techniques, however, the users often experience difficulties in interpreting and exploiting the association rules extracted from large transaction data with high dimensionality. The primary reasons for such difficulties are two-folds. Firstly, too many association rules can be produced by the conventional association rule mining algorithms, and secondly, some association rules can be partly overlapped. This problem can be addressed if the user can select the relevant items to be used in association rule mining, however, there are often quite complex relations among the items in large transaction data. In this context, this paper aims to propose a novel visual exploration tool, structured association map (SAM), which enables the users to find the group of the relevant items in a visual way. The appearance of SAM is similar with the well-known cluster heat map, however, the items in SAM are sorted in more intelligent way so that the users can easily find the interesting area formed by a set of associated items, which are likely to constitute interesting many-to-many association rules. Moreover, this paper introduces an index called S2C, designed to evaluate the quality of SAM, and explains the SAM based association analysis procedure in a comprehensive manner. For illustration, this procedure is applied to a mass health examination result data set, and the experiment results demonstrate that SAM with high S2C value helps to reduce the complexities of association analysis significantly and it enables to focus on the specific region of the search space of association rule mining while avoiding the irrelevant association rules.

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.

We propose an approach for selecting appropriate defuzzification methods that fulfills the requirements of a group of experts.We propose a sensitivity analysis of defuzzification methods.We apply this approach to the assessment of dam performance.Methods were chosen in relation to the objectives (decision-support and communication) defined by a group of dam experts.The approach is applied to a real case. The performance assessment of dams is of major importance for optimizing maintenance management. A methodology is provided here to assess the risk of dam failure using data collected during in-situ inspections and from the design and follow-up files. This assessment takes into account uncertainty associated with the data and the assessment process. The result of this assessment is introduced in a fuzzy frame. This paper presents the approach taken to choose defuzzification methods that allow extracting the information necessary from this possibility distribution so that dam experts can both rank dam maintenance actions and communicate the results of this assessment.This paper first presents the process used to assess dam performance after which it presents the defuzzification methods available. A sensitivity analysis is then performed to select the methods most relevant for our case study. The last part presents the development of a tool useful for the application of defuzzification methods to our problematic and describes a real case study in which the selected defuzzification methods are used.

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.

A novel method for the assessment of trademark similarity is proposed.The method blends together visual, semantic and phonetic similarity.It produces an aggregated score based on the individual assessments.Evaluation using information retrieval measures and human judgment. Trademarks are intellectual property assets with potentially high reputational value. Their infringement may lead to lost revenue, lower profits and damages to brand reputation. A test normally conducted to check whether a trademark is highly likely to infringe other existing, already registered, trademarks is called a likelihood of confusion test. One of the most influential factors in this test is establishing similarity in appearance, meaning or sound. However, even though the trademark registration process suggests a multi-faceted similarity assessment, relevant research in expert systems mainly focuses on computing individual aspects of similarity between trademarks. Therefore, this paper contributes to the knowledge in this field by proposing a method, which, similar to the way people perceive trademarks, blends together the three fundamental aspects of trademark similarity and produces an aggregated score based on the individual visual, semantic and phonetic assessments. In particular, semantic similarity is a new aspect, which has not been considered by other researchers in approaches aimed at providing decision support in trademark similarity assessment. Another specific scientific contribution of this paper is the innovative integration, using a fuzzy engine, of three independent assessments, which collectively provide a more balanced and human-centered view on potential infringement problems. In addition, the paper introduces the concept of degree of similarity since the line between similar and dissimilar trademarks is not always easy to define especially when dealing with blending three very different assessments. The work described in the paper is evaluated using a database comprising 1400 trademarks compiled from a collection of real legal cases of trademark disputes. The evaluation involved two experiments. The first experiment employed information retrieval measures to test the classification accuracy of the proposed method while the second used human collective opinion to examine correlations between the trademark scoring/rating and the ranking of the proposed method, and human judgment. In the first experiment, the proposed method improved the F-score, precision and accuracy of classification by 12.5%, 35% and 8.3%, respectively, against the best score computed using individual similarity. In the second experiment, the proposed method produced a perfect positive Spearman rank correlation score of 1.00 in the ranking task and a pairwise Pearson correlation score of 0.92 in the rating task. The test of significance conducted on both scores rejected the null hypotheses of the experiment and showed that both scores correlated well with collective human judgment. The combined overall assessment could add value to existing support systems and be beneficial for both trademark examiners and trademark applicants. The method could be further used in addressing recent cyberspace phenomena related to trademark infringement such as customer hijacking and cybersquatting.

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.

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.

Proposed XPath predicate enrichments for wrapper induction approach.Built on generalisation strategy that aligns and merge XPaths.Focus on taking full advantage of XPath syntax for wrapper construction.Test data (PostgreSQL db) supplied, based on the work of Hao et?al. (2011).Method can be used to merge data from various heterogeneous sources. Extracting data from various semi-structured sources is a topic that has received a lot of attention. Wrapper induction specifically has been studied extensively, where users annotate a couple of data sources with examples of the data they want, after which a procedure (wrapper) is constructed that can optimally extract similar data as well. In this paper a novel wrapper induction approach is proposed, exploiting the premise of the general applicability of the XPath query language, studied specifically within the context of web pages. After a user annotates a limited set of web pages with the required data, a generalised XPath is constructed that is capable of extracting the examples and, optimally, similar data as well. This generalised baseline XPath is then enriched with predicates, based on context and structure of the data sources, to optimise the precision/recall balance of the data extraction capability of the wrapper. Six variations of such limiting predicates are introduced and investigated. In this paper, it is shown that the baseline approach often generalises the samples too much, leading to a decreased precision. Enriching the baseline wrapper by the addition of predicates limits the generalisation power of the queries in an intelligent manner. Experimental results show that there is a significant improvement in the overall precision of the generalised query, without an excessive loss in recall. Documented tests and real world experience with a large amount of data show that the technique is flexible, easily understood and applicable in a broad range of applications. It is not only of interest in the fields of web information retrieval, but can also be used in the contexts of, e.g., reverse engineering of databases, ontology expansion and deep web data mining, as both simple lists of data and complex structures can be extracted.

This paper presents an online portfolio selection algorithm based on pattern matching principle where it makes a decision on the optimal portfolio in each period and updates the optimal portfolio at the beginning of each period. The proposed method consists of two steps: i) sample selection, ii) portfolio optimization. First, in the sample selection, clustering algorithms including k-means, k-medoids, spectral and hierarchical clustering are applied to discover time windows (TW) similar to the recent time window. Then, after finding the similar time windows and predicting the market behavior of the next day, the optimum function along with the transaction cost is used in the portfolio optimization step in which, four algorithms including KMNLOG, KMDLOG, SPCLOG and HRCLOG are proposed for this purpose. The presented algorithms are applied on 5 different datasets with different characteristics including different markets, stocks, and time periods, and their performance has been evaluated. The results show that the provided algorithms in this paper, have better performance in terms of efficiency compared to the algorithms provided in the literature.

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.