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A Single-valued neutrosophic set (SVNS) has recently been explored as a comprehensive tool to assess uncertain information due to varied human cognition. This notion stretches the domain of application of the classical fuzzy set and its extended versions. Various comparison measures based on SVNSs like distance measure, similarity measure, and, divergence measure have practical significance in the study of clustering analysis, pattern recognition, machine learning, and computer vision-related problems. Existing measures have some drawbacks in terms of precision and exclusion of information and produce unreasonable results in categorization problems. In this paper, we propose a generic method to define new divergence measures based on common aggregation operators and discuss some algebraic properties of the proposed divergence measures. To further appreciate the proposed divergence measures, their application to pattern recognition has been investigated in conjunction with the prominent existing comparison measures based on SVNSs. The comparative assessment sensitivity analysis of the proposed measures establishes their edge over the existing ones because of appropriate classification results. [ABSTRACT FROM AUTHOR]

In order to better evaluate and promote human health, this paper analyzes the influence of different inertial-measurement-unit signals, different sensor locations, different activity intensities and different signal fusion schemes on the accuracy of physical strength consumption estimation during walking and running activities. Different pattern recognition methods, such as the Counts-based linear regression model, the typical non-linear model based on decision tree and artificial neural network, and the end-to-end convolutional neural network model, are analyzed and compared. Our findings are as follows: 1) For the locations of sensors during walking and running activities, the physical strength consumption prediction accuracy at the ankle location is higher than that at the hip location. Therefore, wearing an inertial-measurement-unit at the ankle can improve the accuracy of the model. 2) Regarding the types of activity signals during walking and running activities, the impact of accelerometer signals on hip and ankle prediction accuracy is not significantly different, while the gyroscope model is more sensitive to the location, with higher prediction accuracy at the ankle than at the hip. In addition, the physical strength consumption prediction accuracy of accelerometer signals is higher than that of gyroscope signals, and fusion of accelerometer and gyroscope signals can improve the accuracy of physical strength consumption prediction. 3) For different data analysis models during walking and running activities, the artificial neural network model that integrates different sensor locations and inertial-measurement-unit signals with different activity intensities has the lowest mean squared error for the measurement of physical strength consumption. The non-linear models based on decision tree and artificial neural network have better physical strength consumption prediction capabilities than the Counts-based linear regression model, especially for high-intensity activity energy consumption prediction. In addition, feature engineering models are generally better than convolutional neural network model in terms of overall performance and prediction results under the three different activity intensities. Furthermore, as the activity intensity increases, the performance of all physical strength consumption calculation models decreases. We recommend using the artificial neural network model based on multi-signal fusion to estimate physical strength consumption during walking and running activities because this model exhibits strong generalization ability in cross-validation and test results, and its stability under different activity intensities is better than that of the other three models. To the best of our knowledge, this paper is the first to delve deeply and in detail into methods for estimating physical strength consumption. Undoubtedly, our paper will have an impact on research related to topics such as intelligent wearable devices and subsequent methods for estimating physical strength consumption, which are directly related to physical health. [ABSTRACT FROM AUTHOR]

This research focused on Real-time MRI lung images that were revealed using three grade processes by manipulating nanophotonics components, mapping by deep learning, machine learning, and pattern recognition. This research is Solving Magnetic resonance imaging of interstitial lung diseases with Hybrid feedforward Deep Neural Network (ffDNN) and Convolutional Neural Network (CNN) architecture. The feedforward deep neural network (ffDNN) and Convolutional Neural Network (CNN) techniques are used to Solving Magnetic resonance imaging of interstitial lung diseases on the nanophotonics components, deep learning, and machine learning Platform. The Proposed semiconductor monolithic integration approach employed for bio-Magnetic resonance imaging characterization using photonic crystal "Symptomatic Image Revealing" details of the resonant monolithic. The proposed machine-learning-based approach revealed characterizing multi-parameter design space of nanophotonic components using Nano-optic imagers. The Pattern Recognition for MRI was performed for lower dimensionality. Finally, the Hybrid feedforward Deep Neural Network (ffDNN) and Convolutional Neural Network (CNN) architecture for calculating the height and size of scatterers using the inverse design of the meta-optical structure. The temporal resolution assessment of image data pixel size 280x360 hyperspectral imaging temporal resolution is 25, and magnetic resonance imaging temporal resolution is 50. The Image distribution shows that phase shift and transmission are 2.78 degrees and at 95%. The result for the inverse design using CNN returns the efficient inverse design of test data that can be designed according to the required pressure distribution. Wavelength 1000 nanometer to 1600 machine learning method absorbance 40% and ffDNN absorbance 33%. [ABSTRACT FROM AUTHOR]

Fuzzy clustering is an important research field in pattern recognition, machine learning and image processing. The fuzzy C-means (FCM) clustering algorithm is one of the most common fuzzy clustering algorithms. However, it requires a given number of clusters in advance for accurate clustering of data sets, so it is necessary to put forward a better clustering validity index to verify the clustering results. This paper presents a ratio component-wise design method of clustering validity function based on FCM clustering method. By permutation and combination of six clustering validity components representing different meanings in the form of ratio, 49 different clustering validity functions are formed. Then, these functions are verified experimentally under six kinds of UCI data sets, and a clustering validity function with the simplest structure and the best classification effect is selected by comparison. Finally, this function is compared with seven traditional clustering validity functions on eight UCI data sets. The simulation results show that the proposed validity function can better verify the classification results and determine the optimal clustering number of different data sets. [ABSTRACT FROM AUTHOR]

Anomaly detection for sensor systems is one of the most researched topics for the Internet of Thing systems. Researchers have been attracted to machine learning classification problems that are considered the most effective techniques. The novel model is proposed by combining anomaly pattern Symbolic Aggregate Approximation (SAX), processing imbalance data and machine learning techniques for sensor anomaly detection. The advantage of anomaly patterns and machine learning leads to the the proposed model to have better performance. The proposed model consists of three phases: finding anomaly pattern features, processing imbalanced data, exploring data by machine learning model. In this paper, the main contributions with respect to previous works can be listed as follows: (i) Successful modeling the new method of SAX for time series data for finding complex and dynamic anomaly patterns. (ii) Archiving applied anomaly pattern feature into machine learning model Random Forest and hyperparameters optimisation of these model. (iii) Fitfully proposed a model combining SAX, imbalance technique, and random forest to anomaly detection. (iv) Achieving applied proposal model in automatic meter intelligence system in Vietnam. The experiential results of the proposed model have described the robustness and better performance for detecting anomalies of power meter sensors. [ABSTRACT FROM AUTHOR]

High-dimensional multi-label data is widespread in practical applications, which brings great challenges to the research field of pattern recognition and machine learning. Many feature selection algorithms have been proposed in recent years, among which the filtering feature selection algorithm is the most popular one because of its simplicity. Therefore, filtering feature selection has become a hot research topic, especially the multi-label feature selection algorithm based on mutual information. In the algorithm, the computation cost of high dimensional mutual information is expensive. How to approximate high order mutual information based on low order mutual information has become a major research direction. To our best knowledge, all existing feature selection algorithms that consider the label correlation will increase the computational cost greatly. Therefore, this paper proposes an approximation method of three-dimensional interaction information, which is applied to the calculation of correlation and redundancy. It can take the correlation of labels into account and don't increase the computation cost significantly at the same time. Experiments analysis results show that the proposed method is effective. [ABSTRACT FROM AUTHOR]

Handwritten digit recognition is one of the extensively studied areas in machine learning. Apart from the wider research on handwritten digit recognition on MNIST dataset, there are many other research works on various script recognition. However, it is not very common for multi-script digit recognition which encourages the development of robust and multipurpose systems. Additionally, working on multi-script digit recognition enables multi-task learning. It is evident that multi-task learning improves model performance through inductive transfer using the information contained in related tasks. Therefore, in this study multi-script handwritten digit recognition using multi-task learning is proposed to be investigated. As a specific case of demonstrating the solution to the problem, Amharic handwritten character recognition is also experimentally tested. The handwritten digits of three scripts including Latin, Arabic, and Kannada are studied to show that multi-task models with a reformulation of the individual tasks have shown promising results. In this study, a novel approach of using the individual tasks predictions was proposed to help the classification performance. These research findings have outperformed the baseline and the conventional multi-task learning models. More importantly, it avoided the need for weighting the different losses of the tasks, which is one of the challenges in multi-task learning. [ABSTRACT FROM AUTHOR]

In the field of intelligent robot engineering, whether it is humanoid, bionic or vehicle robots, the driving forms of standing, moving and walking, and the consciousness discrimination of the environment in which they are located have always been the focus and difficulty of research. Based on such problems, Naive Bayes Classifier (NBC), Support Vector Machine(SVM), k-Nearest-Neighbor (KNN), Decision Tree (DT), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were introduced to conduct experiments. The six individual classifiers have an obvious effect on a particular type of ground, but the overall performance is poor. Therefore, the paper proposes a "Novel Hybrid Evolutionary Learning" method (NHEL) which combines every single classifier by means of weighted voting and adopts an improved genetic algorithm (GA) to obtain the optimal weight. According to the fitness function and evolution times, this paper designs the adaptively changing crossover and mutation rate and applies the conjugate gradient (CG) to enhance GA. By making full use of the global search capabilities of GA and the fast local search ability of CG, the convergence speed is accelerated and the search precision is upgraded. The experimental results show that the performance of the proposed model is significantly better than individual machine learning and ensemble classifiers. [ABSTRACT FROM AUTHOR]

At present, there are still many deficiencies in Chinese-Japanese machine translation methods, the processing of corpus information is not deep enough, and the translation process lacks rich language knowledge support. In particular, the recognition accuracy of Japanese characters is not high. Based on machine learning technology, this study combines image feature retrieval technology to construct a Japanese character recognition model and uses Japanese character features as the algorithm recognition object. Moreover, this study expands image features by generating a brightness enhancement function using a bilateral grid. In order to exclude the influence of the edge and contour of the image scene on the analysis of the image source, the brightness value of the HDR image is used instead of the pixel value of the image as the image data. In addition, this research designs experiments to study the translation effects of this research model. The research results show that the model proposed in this paper has certain effects and can provide theoretical references for subsequent related research. [ABSTRACT FROM AUTHOR]

Fuzzy English recognition is affected by many factors, which leads to certain accuracy problems in intelligent recognition results. In order to improve the automatic recognition efficiency of fuzzy English, based on machine learning technology, this study constructs a neural network model. At the same time, this paper analyzes the research status and existing problems of handwritten character recognition, analyzes the model, and adopts multiple modules for automatic English recognition. In addition, the system is built on the basis of algorithms and model support, which makes fuzzy English recognition intelligent. Finally, in order to study the algorithm and model performance, the fuzzy English recognition is carried out through experiments. The research shows that the model constructed in this paper has certain recognition effect, which can be applied to practice, and can provide theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

Graph-based algorithms have aroused considerable interests in recent years by facilitating pattern recognition and learning via information propagation process through the graph. Here, we propose an unsupervised learning algorithm based on propagation on bipartite graph, referred to as Propagation in Bipartite Graph (PBG) algorithm. The contributions of this approach are threefold: 1) we present an iterative graph-based algorithm and a straight-forward bipartite representation for textual data, in which vertices represent documents and words, and edges between documents and words represent the occurrences of the words in the documents. Additionally, 2) we show that PBG is more flexible and easier to be adapted for different applications than the mathematical formalism of the generative models, and 3) we present a comprehensive evaluation and comparison of PBG to other topic extraction techniques. Here, we describe the strategy employed in PBG algorithm as a problem of maximization of similarity between latent vectors assigned to vertices and edges and demonstrate that the proposed strategy can be improved by assigning good initial values for the vectors. We notice that PBG can be parallelized by a simple adjustment in the algorithm. We also show that the proposed algorithm is competitive with LDA and NMF in the task of textual collection modelling, returning coherent topics, and in the dimensionality reduction task. [ABSTRACT FROM AUTHOR]

The ability to compare systems from the same domain is of central importance for their introduction into complex applications. In the domains of named entity recognition and entity linking, the large number of systems and their orthogonal evaluation w.r.t. measures and datasets has led to an unclear landscape regarding the abilities and weaknesses of the different approaches. We present Gerbil – an improved platform for repeatable, storable and citable semantic annotation experiments – and its extension since being release. Gerbil has narrowed this evaluation gap by generating concise, archivable, human- and machine-readable experiments, analytics and diagnostics. The rationale behind our framework is to provide developers, end users and researchers with easy-to-use interfaces that allow for the agile, fine-grained and uniform evaluation of annotation tools on multiple datasets. By these means, we aim to ensure that both tool developers and end users can derive meaningful insights into the extension, integration and use of annotation applications. In particular, Gerbil provides comparable results to tool developers, simplifying the discovery of strengths and weaknesses of their implementations with respect to the state-of-the-art. With the permanent experiment URIs provided by our framework, we ensure the reproducibility and archiving of evaluation results. Moreover, the framework generates data in a machine-processable format, allowing for the efficient querying and post-processing of evaluation results. Additionally, the tool diagnostics provided by Gerbil provide insights into the areas where tools need further refinement, thus allowing developers to create an informed agenda for extensions and end users to detect the right tools for their purposes. Finally, we implemented additional types of experiments including entity typing. Gerbil aims to become a focal point for the state-of-the-art, driving the research agenda of the community by presenting comparable objective evaluation results. Furthermore, we tackle the central problem of the evaluation of entity linking, i.e., we answer the question of how an evaluation algorithm can compare two URIs to each other without being bound to a specific knowledge base. Our approach to this problem opens a way to address the deprecation of URIs of existing gold standards for named entity recognition and entity linking, a feature which is currently not supported by the state-of-the-art. We derived the importance of this feature from usage and dataset requirements collected from the Gerbil user community, which has already carried out more than 24,000 single evaluations using our framework. Through the resulting updates, Gerbil now supports 8 tasks, 46 datasets and 20 systems. [ABSTRACT FROM AUTHOR]

This paper presents a learning algorithm for fuzzy neural networks based on unineurons able to generate interpretation provided by the model through fuzzy rules. The learning algorithm is based on ideas from Extreme Learning Machine, to achieve a low time complexity, and pruning method based on F-scores resulting in accurate models using low complexity resources, using only training data in a single step. Experiments considering binary pattern classification are detailed. Results and statistical evaluation suggest the suggested approach as a promising alternative for pattern recognition with a good accuracy and some level of interpretability through a process of pruning performed in simple steps. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) has been well applied in pattern recognition, classification and machine learning thanks to its high performance. Most ANNs are designed by a static structure whose weights are trained during a learning process by supervised or unsupervised methods. These training methods require a set of initial weights values, which are normally randomly generated, with different initial sets of weight values leading to different convergent ANNs for the same training set. Dealing with these drawbacks, a trend of dynamic ANN was invoked in the past year. However, they are either too complex or far from practical applications such as in the pathology predictor in binary multi-input multi-output (MIMO) problems, when the role of a symptom is considered as an agent, a pathology predictor's outcome is formed by action of active agents while other agents' activities seem to be ignored or have mirror effects. In this paper, we propose a new dynamic structural ANN for MIMO problems based on the dependency graph, which gives clear cause and result relationships between inputs and outputs. The new ANN has the dynamic structure of hidden layer as a directed graph showing the relation between input, hidden and output nodes. The properties of the new dynamic structural ANN are experienced with a pathology problem and its learning methods' performances are compared on a real well known dataset. The result shows that both approaches for structural learning process improve the quality of ANNs during learning iteration. [ABSTRACT FROM AUTHOR]

Older patients with depression or Alzheimer's disease (AD) at the stage of early dementia or mild cognitive impairment may present with objective cognitive impairment, although the pathology and thus therapy and prognosis differ substantially. In this study, we assessed the potential of an automated algorithm to categorize a test set of 65 T1-weighted structural magnetic resonance images (MRI). A convenience sample of elderly individuals fulfilling clinical criteria of either AD (n = 28) or moderate and severe depression (n = 37) was recruited from different settings to assess the potential of the pattern recognition method to assist in the differential diagnosis of AD versus depression. We found that our algorithm learned discriminative patterns in the subject's grey matter distribution reflected by an area under the receiver operator characteristics curve of up to 0.83 (confidence interval ranged from 0.67 to 0.92) and a balanced accuracy of 0.79 for the separation of depression from AD, evaluated by leave-one-out cross validation. The algorithm also identified consistent structural differences in a clinically more relevant scenario where the data used during training were independent from the data used for evaluation and, critically, which included five possible diagnoses (specifically AD, frontotemporal dementia, Lewy body dementia, depression, and healthy aging). While the output was insufficiently accurate to use it directly as a means for classification when multiple classes are possible, the continuous output computed by the machine learning algorithm differed between the two groups that were investigated. The automated analysis thus could complement, but not replace clinical assessments. [ABSTRACT FROM AUTHOR]

Probabilistic neural networks (PNNs) are artificial neural network algorithms widely used in pattern recognition and classification problems. In the traditional PNN algorithm, the probability density function (PDF) is approximated using the entire training dataset for each class. In some complex datasets, classmate clusters may be located far from each other and these distances between clusters may cause a reduction in the correct class's posterior probability and lead to misclassification. This paper presents a novel PNN algorithm, the competitive probabilistic neural network (CPNN). In the CPNN, a competitive layer ranks kernels for each class and an optimum fraction of kernels are selected to estimate the class-conditional probability. Using a stratified, repeated, random subsampling cross-validation procedure and 9 benchmark classification datasets, CPNN is compared to both traditional PNN and the state of the art (e.g. enhanced probabilistic neural network, EPNN). These datasets are examined with and without noise and the algorithm is evaluated with several ratios of training to testing data. In all datasets (225 simulation categories), performance percentages of both CPNN and EPNN are greater than or equivalent to that of the traditional PNN; in 73% of simulation categories, the CPNN analyses show modest improvement in performance over the state of the art. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a novel traffic sign recognition algorithm based on sparse representation and dictionary learning. In the past period of research and applications of traffic sign recognition, most of the traffic sign recognition algorithms are based on statistical learning, neural networks and template matching algorithm. In these algorithms, they need high-dimensional mapping during classification, resulting in huge amount of calculation. Meanwhile, when the external environment changes, such as illumination, deformation and occlusion, the recognition rate will be further reduced. The proposed sparse representation theory has much better performance to solve the problems of external environment changed and while we use dictionary learning method to build a traffic sign over-complete redundant dictionary, the experimental results clearly showed that the algorithm we proposed has much better performance than traditional algorithm and also has much higher recognition rates. [ABSTRACT FROM AUTHOR]

Rough set theory is a mathematical framework that can be visualized as a soft computing tool dealing with the vagueness and uncertainty of data and is applied to pattern recognition, data mining, and knowledge discovery. Document clustering is another area of research with values which are a bag of words that describe contents within clusters. This work analyzes how rough set theory is used for document clustering to fix issues that clustering methods manage. In this survey, an exhaustive literature reviewof the concept of rough sets, as well as howthe lower and upper approximation of a set can be used for document clustering, has been presented. Rough set clusters are shown to be useful for representing real-time applications such as biomedical inferences, network data handling, and citation analysis. The survey is done in phases, showing how machine learning algorithms have been incorporated for document clustering using rough set theory, as well as how rough set theory has been extended to adapt to document clustering with feature selection techniques and feature/dimensionality reduction and, finally, ending with a view of assorted clustering tasks where rough set theory is applied. The classification of rough set theory for document clustering is depicted and its applications presented in this paper. The rough set theory works with resolving ambiguity and uncertainty in data. To the best of our knowledge, a rough set clustering survey has not been done earlier in the literature reviewed and the survey ends with a critical analysis of rough set theory in each application of clustering. [ABSTRACT FROM AUTHOR]

Linear Discriminant Analysis (LDA) is a very common technique for dimensionality reduction problems as a preprocessing step for machine learning and pattern classification applications. At the same time, it is usually used as a black box, but (sometimes) not well understood. The aim of this paper is to build a solid intuition for what is LDA, and how LDA works, thus enabling readers of all levels be able to get a better understanding of the LDA and to know how to apply this technique in different applications. The paper first gave the basic definitions and steps of how LDA technique works supported with visual explanations of these steps. Moreover, the two methods of computing the LDA space, i.e. class-dependent and class-independent methods, were explained in details. Then, in a step-by-step approach, two numerical examples are demonstrated to show how the LDA space can be calculated in case of the class-dependent and class-independent methods. Furthermore, two of the most common LDA problems (i.e. Small Sample Size (SSS) and non-linearity problems) were highlighted and illustrated, and stateof-the-art solutions to these problems were investigated and explained. Finally, a number of experiments was conducted with different datasets to (1) investigate the effect of the eigenvectors that used in the LDA space on the robustness of the extracted feature for the classification accuracy, and (2) to show when the SSS problem occurs and how it can be addressed. [ABSTRACT FROM AUTHOR]

In this paper, we define covering-based rough membership function for the tenth type of covering-based rough sets which was examined by Tsang et al. and obtain some properties about the rough membership function which bases on ®C10 as well as characterize the covering-based rough set approximation operators from the viewpoint of numerical values. An example in evidence-based medicine are proposed to illustrate the practical application backgrounds of Pawlak's rough membership function in real life. By this example, we can show that this covering-based rough membership function is more realistic than Pawlak's rough membership function in applications of real life. [ABSTRACT FROM AUTHOR]

DNA microarrays is a technology that can be used to diagnose cancer and other diseases. To automate the analysis of such data, pattern recognition and machine learning algorithms can be applied. However, the curse of dimensionality is unavoidable: very few samples to train, and many attributes in each sample. As the predictive accuracy of supervised classifiers decays with irrelevant and redundant features, the necessity of a dimensionality reduction process is essential. The main idea is to retain only the genes that are the most influential in the classification of the disease. In this paper, a new methodology based on Principal Component Analysis and Logistics Regression is proposed. Our method enables the selection of particular genes that are relevant for classification. Experiments were run using eight different classifiers on two benchmark datasets: Leukemia and Lymphoma. The results show that our method not only reduces the number of required attributes, but also increase the classification accuracy in more than 10% in all the cases we tested. [ABSTRACT FROM AUTHOR]

The need to reduce the dimensionality of a dataset whilst retaining its inherent manifold structure is key to many pattern recognition, machine learning, and computer vision problems. This process is often referred to as manifold learning since the structure is preserved during dimensionality reduction by learning the intrinsic low-dimensional manifold that the data lies upon. In this paper a heuristic approach is presented to tackle this problem by approximating the manifold as a set of piecewise linear models. By merging these linear models in an order defined by their global topology a globally stable and locally accurate model of the manifold can be obtained. A detailed analysis of the proposed approach is presented along with comparison with existing manifold learning techniques. Results obtained on both artificial and image based data show that in many cases this heuristic approach to manifold learning is able to out-perform traditional techniques. [ABSTRACT FROM AUTHOR]

Twin support vector machines are a recently proposed learning method for binary classification. They learn two hyperplanes rather than one as in conventional support vector machines and often bring performance improvements. Multiview learning is concerned about learning from multiple distinct feature sets, which aims to exploit distinct views to improve generalization performance. In this paper, we propose multi-view twin support vector machines by solving a pair of quadratic programming problems. This paper gives a detailed derivation of the Lagrange dual optimization formulation. The linear multiview twin support vector machines are further generalized to the nonlinear case by the kernel trick. Experimental results demonstrate that our proposed methods are effective. [ABSTRACT FROM AUTHOR]

We are currently working on the realization of a PLS: Polyphony Learning Software tool within the framework of a pluridisciplinary project for promoting and preserving the Corsican culture. In this article we shall describe the different technical choices adopted so as to develop this learning tool based upon a gestural language. This software should enable a single singer to create polyphony. This is possible by replacing the other singers with singing voices. Our problematic is to associate artificial voices to each of the language gestures. This process has been broken down into several steps: defining a gestural language, gesture capture and recognition, association of a singing voice. [ABSTRACT FROM AUTHOR]

The Mahalanobis distance (MD) is a distance widely used in Statistics, Machine Learning and Pattern Recognition. When the data come from a Gaussian distribution, the MD uses the covariance matrix to evaluate the distance between a data point and the distribution mean. In this paper, we generalize the MD for general unimodal distributions, introducing a particular class of Mercer kernel, the density kernel, based on the underlying data density. Density kernels induce distances that generalize the MD and that are useful when data do not fit to the Gaussian distribution. We study the theoretical properties of the proposed distance and show its performance on a variety of artificial and real data analysis problems. [ABSTRACT FROM AUTHOR]

This paper introduces a neuro-fuzzy framework for handling multi-class classification problems. Instead of decomposing such problems into simple sub-problems and solving each part using a different classifier, the proposed system decomposes and implements the entire problem automatically in the same framework. The decomposition is performed using the most commonly used methods for dividing multi-class classification problems: OAA (one-against-all) and OAO (one-against-one). Consequently, two models are introduced: OAA and OAO based neuro-fuzzy classifiers. The design of the proposed models is based on the implementation of each sub-problem using a set of weights. The learning is performed by adjusting every set independently, and without adjusting the parameters of membership functions. This considerably simplifies the classification and learning tasks. After the learning stage, the proposed systems act as a single-module classifier for recognizing new examples. [ABSTRACT FROM AUTHOR]

Feature selection plays an important role in data mining, machine learning and pattern recognition, especially for large scale data with high dimensions. Many selection techniques have been proposed during past years. Their general purposes are to exploit certain metric to measure the relevance or irrelevance between different features of data for certain task, and then select fewer features without deteriorating discriminative capability. Each technique, however, has not absolutely better performance than others' for all kinds of data, due to the data characterized by incorrectness, incompleteness, inconsistency, and diversity. Based on this fact, this paper put forward to a new scheme based on partition clustering for feature selection, which is a special preprocessing procedure and independent of selection techniques. Experimental results carried out on UCI data sets show that the performance achieved by our proposed scheme is better than selection techniques without using this scheme in most cases. [ABSTRACT FROM AUTHOR]

Increasing growth of task-oriented texts specifically in organizations, have become a catastrophe nowadays. To overcome this problem, potential classification methods are improved. This paper outlines the capability of neuro-fuzzy approach and artificial immune recognition systems to enhance task-oriented texts classification. Task-oriented texts stand for various kinds of texts which are organized to help the users with their different tasks such as: research, development, learning, justification, innovation and analysis. In this respect, seven major attributes with three nominal values of low, medium and high are considered to classify text into six task classes. To illustrate the capabilities of proposed approaches, Takagi-Sugeno as a neuro-fuzzy approach using lolimot learning algorithm, is compared with multilayer perceptron (MLP), and Radial Basis Function (RBF). In the meantime, various versions of Artificial Immune Recognition Systems (AIRS) including AIRS1, AIRS2, Parallel AIRS and Modified AIRS with Fuzzy K-Nearest neighbor (Fuzzy-KNN) are also evaluated in comparison with the above mentioned algorithms to classify the same text. The experimental results of classification on a dataset of 540 data reveals that, due to the distributed characteristics of a text, and the complexity of tasks respectively, evolutionary and neuro-fuzzy methods are expected to be particularly workable and successfully applicable to task-oriented text classification specifically for the purpose of decision support. [ABSTRACT FROM AUTHOR]

Twin support vector machine classifier (TWSVM) was proposed by Jayadeva et al., which was used for binary classification problems. TWSVM not only overcomes the difficulties in handling the problem of exemplar unbalance in binary classification problems, but also it is four times faster in training a classifier than classical support vector machines. This paper proposes one-versus-all twin support vector machine classifiers (OVA-TWSVM) for multi-category classification problems by utilizing the strengths of TWSVM. OVA-TWSVM extends TWSVM to solve k-category classification problems by developing k TWSVM where in the ith TWSVM, we only solve the Quadratic Programming Problems (QPPs) for the ith class, and get the ith nonparallel hyperplane corresponding to the ith class data. OVA-TWSVM uses the well known one-versus-all (OVA) approach to construct a corresponding twin support vector machine classifier. We analyze the efficiency of the OVA-TWSVM theoretically, and perform experiments to test its efficiency on both synthetic data sets and several benchmark data sets from the UCI machine learning repository. Both the theoretical analysis and experimental results demonstrate that OVA-TWSVM can outperform the traditional OVA-SVMs classifier. Further experimental comparisons with other multiclass classifiers demonstrated that comparable performance could be achieved. [ABSTRACT FROM AUTHOR]

This paper summarizes the some of the recent developments in the area of application of rough sets and granular computing in hierarchical learning. We present the general framework of rough set based hierarchical learning. In particular, we investigate several strategies of choosing the appropriate learning algorithms for first level concepts as well as the learning methods for the intermediate concepts. We also propose some techniques for embedding the domain knowledge into the granular, layered learning process in order to improve the quality of hierarchical classifiers. This idea, which has been envisioned and developed by professor Andrzej Skowron over the last 10 years, shows to be very efficient in many practical applications. Throughout the article, we illustrate the proposed methodology with three case studies in the area of pattern recognition. The studies demonstrate the viability of this approach for such problems as: sunspot classification, hand-written digit recognition, and car identification. [ABSTRACT FROM AUTHOR]

The paper aims at tight upper bounds on the size of pattern classification circuits that can be used for a priori parameter settings in a machine learning context. The upper bounds relate the circuit size S(C) to nL := [log2mL], where mL is the number of training samples. In particular, we show that there exist unbounded fan-in threshold circuits with less than (a) SRcc := 2·√2nL + 3 gates for unbounded depth, (b) SLcc := 34.8 · √2nL + 14 · nL - 11 · log2nL + 2 gates for small bounded depth, where in both cases all mL samples are classified correctly. We note that the upper bounds do not depend on the length n of input (sample) vectors. Since nL << n in real-world problem settings, the upper bounds return values that are suitable for practical applications. We provide experimental evidence that the circuit size estimations work well on a number of pattern classification tasks. As a result, we formulate the conjecture that [1.25 · SRcc or [0.07 · SLcc] gates are sufficient to achieve a high generalization rate of bounded-depth classification circuits. [ABSTRACT FROM AUTHOR]

Studying human activities has significant implication in human beneficial applications such as personal healthcare. This research has been facilitated by the development of sensor technologies in pervasive computing with a large quantity of observational data collected about environments and user actions. By mining these data, traditional machine learning techniques have made great progress in recognising activities, but due to the increasing number of sensors and complexity of activities, they are subject to feasibility and scalability. These techniques may benefit from the inclusion of semantic information about the nature and relationships of sensor data and activities being observed. We introduce a new data structure, the context lattice, which allows designers to capture and explore this sort of knowledge. We demonstrate how context lattices can be used to infer human activities with the inclusion of such knowledge. We present comprehensive evaluations of the system against two third-party smart-home data sets, and demonstrate that our approach compares favourably with traditional analytic techniques in many circumstances. We conclude with a discussion of the strengths and weaknesses of context lattices in activity recognition. [ABSTRACT FROM AUTHOR]

In this study we consider the classification (supervised learning) problem in [0 1]^n that utilizes fuzzy sets as pattern classes. Each class is described by one or more fuzzy hyperbox defined by their corresponding minimum- and maximum vertices and the hyperbox membership function. Two types of hyperboxes are created: inclusion hyperboxes that contain input patterns belonging to the same class, and exclusion hyperboxes that contain patterns belonging to two or more classes, thus representing contentious areas of the pattern space. With these two types of hyperboxes each class fuzzy set is represented as a union of inclusion hyperboxes of the same class minus a union of exclusion hyperboxes. The subtraction of sets provides for efficient representation of complex topologies of pattern classes without resorting to a large number of small hyperboxes to describe each class. The proposed fuzzy hyperbox classification is compared to the original Min-Max Neural Network and the Gene ral Fuzzy Min-Max Neural Network and the origins of the improved performance of the proposed classification are identified. These are verified on a standard data set from the Machine Learning Repository. [ABSTRACT FROM AUTHOR]

Soft Computing is an interdisciplinary area that encompasses a variety of computing paradigms. Examples of some popular soft computing paradigms include fuzzy computing, neural computing, evolutionary computing, and probabilistic computing. Soft computing paradigms, in general, aim to produce computing systems/machines that exhibit some useful properties, e.g. making inference with vague and/or ambiguous information, learning from noisy and/or incomplete data, adapting to changing environments, and reasoning with uncertainties. These properties are important for the systems/machines to be useful in assisting humans in our daily activities. Indeed, soft computing paradigms have been demonstrated to be capable of tackling a wide range of problems, e.g. optimization, decision making, information processing, pattern recognition, and intelligent data analysis. A number of papers pertaining to some recent advances in theoretical development and practical application of different soft computing paradigms are highlighted in this special issue. [ABSTRACT FROM AUTHOR]