Search

In this study, reinforced fuzzy radial basis function neural networks (FRBFNN) classifier driven by feature extracted data completed with the aid of effectively preprocessing techniques and evolutionary optimization, and its comprehensive design methodology are introduced. An Overall structure of the reinforced FRBFNN comprises the preprocessing part, the premise part and the consequence part of fuzzy rules of the network. In the preprocessing part, four types of preprocessing algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), combination of PCA and LDA (Hybrid PCA) and fuzzy transform are considered. To extract feature data suitable to characterize signal data, the feature extraction of information data is carried out through the dimensionality reduction done by the preprocessing technique, and then the reduced data are used as the input to the FRBFNN classifier. In the premise part of fuzzy rules of the network, the number of fuzzy rules is determined according to the number of clusters by fuzzy c-means (FCM) clustering. The fitness values of individual fuzzy rules are obtained based on data distribution. In the consequence part of fuzzy rules of the network, the parameters of connection weights located between the hidden layer and the output layer of FRBFNN classifier are estimated by means of the least square estimation. Particle swarm optimization (PSO) is exploited for structural as well as parametric optimization in the FRBFNN classifier. The parameters to be optimized by PSO are related to six factors such as the determination of whether to use data preprocessing, the type of data preprocessing technique, the number of input variables reduced by the preprocessing technique, fuzzification coefficient and the number of fuzzy rules used in fuzzy c-means (FCM) clustering, and the type of connection weights. By using diverse benchmark dataset obtained from UCI repository, the classification performance of the reinforced FRBFNN classifier was evaluated. Through a variety of classification algorithms existed in the Weka data mining software (Weka), the classification performance of the reinforced FRBFNN classifier was compared as well. The superiority of the proposed classifier is demonstrated through Friedman test. Furthermore, we assessed the classification performance of the reinforced FRBFNN classifier applied to black plastic wastes spectral data acquired from Raman and Laser induced breakdown spectroscopy equipment for the practical application of the material sorting system of the black plastic wastes. [ABSTRACT FROM AUTHOR]

This study elaborates on a novel Hidden Markov Model (HMM)-based fuzzy model for time series prediction. Fuzzy rules (rule-based models) are employed to describe and quantify the relationship between the input and output time series, while the HMM is regarded as a vehicle for efficiently capturing the temporal behavior or changes of the multivariate time series which are not capable to capture through commonly encountered fuzzy rule-based models. Essentially, the proposed strategies control the contribution of different fuzzy rules so that the proposed model can well model the dynamic behavior of time series. Fuzzy C-Means clustering technique is an alternative way to construct fuzzy rules. Particle swarm optimization serves as a tool to optimize the parameters of the model (e.g., transition matrix and emission matrix). We construct and investigate the performance of the HMM-based fuzzy model by using a series of synthetic and publicly available multivariate time series. Experimental results demonstrate that the proposed model shows better performance than the fuzzy rule-based models used without the involvement of HMMs. [ABSTRACT FROM AUTHOR]

In group decision-making problems, experts often form multiple subgroups depending upon their backgrounds or attitudes. To explore the effect of different types of subgroups' formation on the final decision and to make the decision process more conforming to human custom, we propose a comprehensive study on the effect of "multi-subgroup background" on a basis of weights optimization and Granular Computing techniques. Firstly, a comparison of different types of clustering strategies is realized to find the most suitable method for clustering experts. Since Analytical Hierarchy Process is selected as the fundamental model, it becomes a reciprocal matrices' or eigenvectors' clustering task. Secondly, allocation strategies of information granularity to each expert along with weight to each subgroup are carefully designed and optimized. Information granularity is viewed as a design asset to provide experts with some flexibility to adjust their evaluation results, whereas weights reflect the importance degrees of different subgroups and further help to increase the consensus. A granulation-degranulation process is developed to evaluate the performance of a set of weights and granularities under a multi-criteria objective's guidance. Thirdly, a proper evolutionary optimization method like particle swarm optimization is redesigned to iteratively generate the best set of information granularities and weights under an equality constraint condition. A series of numerical studies on synthetic data and real-world data are executed to verify the effectiveness of our method. The results show same trend present across experts. [ABSTRACT FROM AUTHOR]

In this paper, we propose a technique to solve LR-bipolar fuzzy linear system(BFLS), LR-complex bipolar fuzzy linear (CBFL) system with real coefficients and LR-complex bipolar fuzzy linear (CBFL) system with complex coefficients of equations. Initially, we solve the LR-BFLS of equations using a pair of positive (∗) and negative (∙) of two n × n LR-real linear systems by using mean values and left-right spread systems. We also provide the necessary and sufficient conditions for the solution of LR-BFLS of equations. We illustrate the method by using some numerical examples of symmetric and asymmetric LR-BFLS equations and obtain the strong and weak solutions to the systems. Further, we solve the LR-CBFL system of equations with real coefficients and LR-CBFL system of equations with complex coefficients by pair of positive (∗) and negative (∙) two n × n real and complex LR-bipolar fuzzy linear systems by using mean values and left-right spread systems. Finally, we show the usage of technique to solve the current flow circuit which is represented by LR-CBFL system with complex coefficients and obtain the unknown current in term of LR-bipolar fuzzy complex number. [ABSTRACT FROM AUTHOR]

In numerous real-world problems, we are faced with difficulties in learning from imbalanced data. The classification performance of a "standard" classifier (learning algorithm) is evidently hindered by the imbalanced distribution of data. The over-sampling and under-sampling methods have been researched extensively with the aim to increase the predication accuracy over the minority class. However, traditional under-sampling methods tend to ignore important characteristics pertinent to the majority class. In this paper, a novel under-sampling method based on information granules is proposed. The method exploits the concepts and algorithms of granular computing. First, information granules are built around the selected patterns coming from the majority class to capture the essence of the data belonging to this class. In the sequel, the resultant information granules are evaluated in terms of their quality and those with the highest specificity values are selected. Next, the selected numeric data are augmented by some weights implied by the size of information granules. Finally, a support vector machine and a K-nearest-neighbor classifier, both being regarded here as representative classifiers, are built based on the weighted data. Experimental studies are carried out using synthetic data as well as a suite of imbalanced data sets coming from the public machine learning repositories. The experimental results quantify the performance of support vector machine and K-nearest-neighbor with under-sampling method based on information granules. The results demonstrate the superiority of the performance obtained for these classifiers endowed with conventional under-sampling method. In general, the improvement of performance expressed in terms of G-means is over 10% when applying information granule under-sampling compared with random under-sampling. [ABSTRACT FROM AUTHOR]

This paper mainly aims to introduce Quintuple Implication Principle (QIP) on interval-valued intuitionistic fuzzy sets (IVIFSs). Firstly, some algebraic properties of a class of interval-valued intuitionistic triangular norms are discussed in detail. In particular, a unified expression of residual interval-valued intuitionistic fuzzy implications generated by left-continuous triangular norms is presented. Secondly, Triple Implication Principles (TIPs) of both interval-valued intuitionistic fuzzy modus ponens (IVIFMP) and fuzzy modus tollens (IVIFMT) based on residual interval-valued intuitionistic fuzzy implications are analyzed. It is shown that the TIP solution of IVIFMP is recoverable, and the TIP solution of IVIFMT is only weakly local recoverable. Moreover, it sees by an illustrated example that the TIP method sometimes makes the computed solutions for IVIFMP and IVIFMT meaningless or misleading. To avoid the above shortcoming and enhance the recovery property of TIP solution of IVIFMT, QIP and α -QIP for IVIFMP and IVIFMT are investigated and the corresponding expressions of solutions of them are also given, respectively. In addition, the QIP methods for IVIFMP and IVIFMT are recoverable and sound. Finally, QIP solutions of IVIFMP for multiple fuzzy rules are provided. An application example for medical diagnosis is given to illustrate the feasibility and effectiveness of the QIP of IVIFMP. [ABSTRACT FROM AUTHOR]

Granular computing, an emerging information processing paradigm transforming complex data into information granules at different scales so that different features and regularities can be revealed, offers an essential linkage between big data and decision making. By using innovative technologies of granular computing that transforms big data collections into information granules, we would be at position of recognizing and exploiting the meaningful pieces of knowledge present in data, and produce sound, and practically supported decisions. In this study, we first summarize a general scheme of big data–granular computing–decision making and then present a case study where we detect the important traffic event information by collecting and analyzing social media data, and transform them into probabilistic information granules that can be used for urban routing navigation. We propose a robust fastest path optimization model to incorporate the impact of traffic events and generate the optimal routing strategy. Real-life experiments are carried out in regional Chaoyang District, Beijing, as well as the backbone roadway network of Beijing, which illustrate the effectiveness of our proposed big data-driven decision-making method. Our study provides new evidence demonstrating that big data can be efficiently used to enhance decisions and granular computing with this regard. The concept of the proposed scheme can be easily extended for decision-making modeling in other domains. [ABSTRACT FROM AUTHOR]

We present a hierarchical model based on the extended fuzzy C-means (EFCM) clustering algorithm to develop a granular view of hotspots on a geographic map. The objective is to establish an overview of the spatial distribution of a phenomenon when the relevant data are partitioned into different datasets. The EFCM algorithm is applied to each dataset to detect local hotspots, represented as circles, on the map. The local hotspots constitute information granules at lower level of abstraction in the model. A weighted EFCM algorithm is then applied to a dataset formed by the centers of all the local hotspots to extract circular prototypes, defined as global hotspots, which constitute information granules at the higher level, and hence, they deliver a global overview of the spatial distribution of the phenomenon on the map. Two indices related to the essential criteria of the principle of justifiable granularity are used. The results demonstrate that the most justifiable overview is obtained by using the radius of the local hotspot as weight. Comparisons with a hierarchical model based on FCM algorithm show that our algorithm gives a better granular view of the phenomenon with respect to the latter. [ABSTRACT FROM AUTHOR]

This study is concerned with the augmentation of rule-based models or fuzzy rule-based models by associating the numeric results produced by them with granular characterization in the form of prediction intervals. The role of prediction intervals encountered in regression analysis is well emphasized in the literature, especially for "monolithic" models such as linear regression or neural networks. However, there have not been comprehensive and algorithmically complete studies devoted to the conceptualization and determination of prediction intervals for rule-based models, Boolean (two-valued) and fuzzy ones. While the results generated by rule-based models are formed by aggregating partial outcomes resulting from the individual rules, the prediction intervals adhere to the same way of aggregation. In this sense, one can regard the rule-based model augmented by the associated prediction intervals as a granular rule-based model. The construction of rule-based models is, in a nutshell, the optimization process being commonly guided by the sum of squared errors (say, RMSE or alike), and the quality of the granular counterpart of the rule-based models is assessed by looking at the quality of prediction intervals and introducing the two pertinent quality measures focused on the granular nature of the obtained results, namely coverage and specificity along with their combined index. In this study, we analyze an impact of the rules and the parameters of fuzzy clustering on the quality of the numeric and granular performance of the models. A series of experimental results is presented to help quantify the performance of granular outputs (prediction intervals) constructed for rule-based models. [ABSTRACT FROM AUTHOR]

Autoencoders are regarded as one of the key functional components of deep learning architectures. In this study, we augment the well-known architectures of autoencoders by incorporating a concept of information granularity, which gives rise to so-called granular autoencoders. It is demonstrated that information granularity can be sought as an essential design asset whose optimal allocation produces the autoencoder with better representation capabilities. Several protocols of allocation of information granularity are presented and assessed with regard to their abilities to represent the data. Selected examples including those dealing with clustering time series are included. [ABSTRACT FROM AUTHOR]

Time-series clustering algorithms have been used in a variety of areas to extract valuable information from complex and massive data sets. However, these algorithms suffer from two shortcomings. On the one hand, most of them are designed for the equal-length time series, while clustering of unequal-length time series is often encountered in real-world problems. On the other hand, commonly used distance measures of time series cannot fully reveal trend differences. To overcome these two shortcomings, this paper focuses on the trend of time series and employs the area-based shape distance to measure their similarity. In addition, we present a new hierarchical clustering for unequal-length time series based on area-based shape distance measure. A series of experiments illustrates the performance of the proposed clustering algorithm. [ABSTRACT FROM AUTHOR]

One of the multimodal biometric scenarios is realized by considering several features coming from a single biometric entity. Dynamic signature verification has been utilized considering such scenarios. We present a new approach, namely probabilistic dynamic time warping, to verify dynamic signatures where we use dynamic time warping in realizing distance determination in the verification process. Signatures are segmented into several segments, where probability of each segment is quantified with the aid of a relative distance associated with two selected threshold levels. The final decision is achieved by combining all segment probabilities using a Bayes rule. Experiments demonstrate improvement of equal error rate for the proposed approach for the random forgery. The method has been tested on synthetic dataset and two publicly available databases of dynamic signatures, namely SCV2004 and MCYT100. [ABSTRACT FROM AUTHOR]

Vehicle license plate (LP) detection is a relatively complex problem until we assume the use of a static camera, variations in illumination, known templates of the LP, guaranteed color patterns and other simple assumptions. Practical applications demand robust and generalized LP detection techniques to accommodate complex scenarios. This work suggests a new approach to solving this problem by treating the vehicle LP as an object. The primary focus of this study is to address following tasks associated with the challenge of LP detection: (1) LP detection in every frame of a video sequence, (2) detection of partial LPs and (3) detection of LPs with moving cameras and moving vehicles. The state-of-the-art object detection techniques, including convolutional neural networks with region proposal (RCNN), its successors (Fast-RCNN and Faster-RCNN) and the exemplar-SVM, are used in this work to provide solutions to the problem. The suggested study demonstrates better results in comprehensive tests and comparisons than other conventional approaches. [ABSTRACT FROM AUTHOR]

When developing models of non-stationary data, it becomes imperative to endow them with some meaningful update mechanisms, viz to provide with sufficient capabilities to accommodate changing characteristics of the environment. These mechanisms make the models of data evolvable. In the study, we introduce and discuss a class of evolvable models of data with the main objective to describe and interpret the incoming data. We advocate that information granularity and ensuing information granules are central to the characterization and interpretation of the dynamics and variability of numeric data. The relevance of information granules describing the data is evaluated in their abilities to construct the associated Takagi-Sugeno rule-based models. It is shown how the condition part of the rules formed by information granules changes when exposed to data of varying characteristics. Along with the structural facet of evolvability discussed is its parametric manifestation present in terms of the changes (updates) of the parameters of the local models standing in the conclusion part of the rules. The continuity of the evolving information granules (being crucial to their interpretability) is assured by running the clustering scheme initialized on the basis of the previously formed clusters (conditions of the rules) rather than starting FCM from some random configuration. We introduce some graph-oriented visualization means to provide a concise insight into the dynamics of information granules. As an interesting alternative, we introduce a granular fuzzy model where the added granularity of the parameters of a stationary fuzzy model is considered as a way to compensate for the non-stationary of the described system. A series of experiments is reported on with intent to demonstrate the performance of the model, analyze mechanisms of evolution of information granules, and deliver some useful comparative analysis. [ABSTRACT FROM AUTHOR]

In this study, we develop a process of estimation of importance of features considered in face recognition by making use of the analytic hierarchy process (AHP). The AHP method of pairwise comparisons realized at three levels of hierarchy becomes crucial to realize a comprehensive weighting of cues so that sound estimates of weights associated with the individual features of faces can be formed. We demonstrate how to carry out an efficient process of face description by using a collection of linguistic descriptors of the features and their groups. Numerical dependencies between the features are quantified with the help of experienced criminology and psychology experts. Finally, we present an entropy-based method of evaluation of the relevance of the estimation process completed by the individuals. The intuitively appealing results of experiments are presented and analyzed in detail. [ABSTRACT FROM AUTHOR]

Supplier selection is a key issue in supply chain management, which directly impacts the manufacturer's performance. The problem can be viewed as a multiple attribute group decision making (MAGDM) that concerns many conflicting evaluation attributes, both being of qualitative and quantitative nature. Due to the increasing complexity and uncertainty of socio-economic environment, some evaluations of attributes are not adequately represented by numerical assessments and type-1 fuzzy sets. In this paper, we develop some linear programming models with the aid of multidimensional analysis of preference (LINMAP) method to solve interval type-2 fuzzy MAGDM problems, in which the information about attribute weights is incompletely known, and all pairwise comparison judgments over alternatives are represented by IT2FSs. First, we introduce a new distance measure based on the centroid interval between the IT2FSs. Then, we construct the linear programming model to determine the interval type-2 fuzzy positive ideal solution (IT2PIS) and corresponding attributes weight vector. Based on it, an extended LINMAP method to solve MAGDM problem under IT2FSs environment is developed. Finally, a supplier selection example is provided to demonstrate the usefulness of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, we focus on the design and refinements of granular pattern classifiers, namely classifiers, which deal with a collection of information granules formed in a certain feature space. The development of this category of classifiers is realized as a two-phase design process. First, information granules occupying some regions of the feature space are formed through invoking mechanisms of clustering or fuzzy clustering. As a result, regions in the feature space are built, which are densely occupied by the patterns predominantly belonging to the same class. We offer a detailed way of assessing the character and quality of information granules and their information (classification-oriented) content. The resulting description is utilized in the realization of the classification mechanism being considered at the second phase of the design of the granular classifier. The mapping from the collection of information granules to class assignment (classification) involves matching of a pattern to be classified to individual information granules and aggregating them by considering the information content of the corresponding granules. In the study, a number of descriptors capturing information content and aggregation functions are analyzed. To improve the performance of the granular classifier, a refinement of information granules is carried out, in which highly heterogeneous information granules (viz. those containing patterns belonging to various classes) are refined (split, specialized), and their refined versions are afterwards used in the buildup of the classifier. A series of experiments involving both synthetic data as well as those publicly available is reported and analyzed, illustrating the main advantages of granular classifiers and their design procedure. [ABSTRACT FROM AUTHOR]

In this study, we are concerned with the formation of interpretable descriptors of dependencies existing in experimental data and realized in the form of linguistic entities (information granules). We elaborate on a way of bridging numerically inclined fuzzy models (in which information granules are built on a basis of experimental data and described through numeric membership functions) and a qualitative way of system modeling (originating from symbol-based modeling). Proceeding with the principles of fuzzy modeling (especially, those residing with rule-based architectures), their numerical constructs of fuzzy sets-information granules are augmented with viable interpretation mechanisms abstracted from the detailed membership functions. In this regard, a linguistic view of the outcomes of fuzzy clustering (realized in terms of fuzzy C-means) are revisited and supplied with an interpretation at a higher level of abstraction. The buildup of the qualitative descriptors embraces two essential aspects of abstraction, namely (a) an abstraction of linguistic terms realized on the basis of membership functions (fuzzy sets) and (b) an abstraction of relationships completed on the basis of detailed functional dependencies present in the fuzzy model (say, the rules of the model). Two categories of problems are studied in detail along with their applications, namely a linguistic description of time series and a linguistic description of linearization tasks. Both of them are illustrated with a number of experimental studies. [ABSTRACT FROM AUTHOR]

In many real-world applications, instances (data) arrive sequentially in the form of streams. Processing such data poses challenges to machine learning. While adhering to on-line learning strategies, in this paper we extend the Flexible Fuzzy Decision Tree (FlexDT) algorithm with multiple partitioning that makes it possible to carry out automatic on-line fuzzy data classification. The proposed method is aimed to balance accuracy and tree size in data stream mining. The objective of the classification problem is to predict the true class of each incoming instances in real time. In terms of evaluation of the method, accuracy, tree depth, and the learning time are significant factors influencing the performance. A series of experiments demonstrate that the proposed method produces optimal trees for both numeric and nominal features (variables). [ABSTRACT FROM AUTHOR]

Information granules, along with their processing constitute a core of granular computing, which provides a unified conceptual and algorithmic framework for designing and analyzing intelligent systems. In this study, we engage a principle of justifiable granularity as a way of forming type-1 and type-2 information granules-granular interval-valued information granules, whose descriptors are intervals themselves rather than numeric entities. A two-phase design process is presented: first, intervals (viz. information granules of type-1) are constructed based on available experimental data. Second, considering the data that have not been 'covered' by the intervals (the data one can refer to as residual granular data), we construct their bounds in the form of information granules (instead of numeric values) thereby giving rise to the concept of granular intervals, namely information granules of type-2. A series of experiments are provided that focus on sensor fusion formed with the aid of information granules and granular system modeling of type-1 and type-2. [ABSTRACT FROM AUTHOR]

A crossover operator in genetic algorithms (GAs) plays an essential role as the main search operator to breed offspring by exchanging information between individuals. Although different types of crossover operators have been developed for real-coded GAs (RCGAs), there has been very little research on combining different crossover operators to build more effective and efficient RCGAs. In this work, we propose new steady-state generation alternation-based RCGAs (SSGAs) ameliorated with (i) an ensemble of different probabilistic variable-wise crossover strategies, which is realized by the corresponding parallel populations, to utilize synergetic and complementary effect with their efficient operations, and (ii) efficient operation at each evolution step to obtain further performance enhancement. To investigate the performance of this ensemble with respect to search abilities and computation time, we compare the proposed algorithms against various SSGAs when running 27 benchmark functions. Empirical studies showed that the proposed algorithms exhibit better performance than the contestant SSGAs on these functions. Moreover, a comparison with the state-of-the-art evolutionary algorithms on eight difficult benchmark functions clearly demonstrated outperformance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The study is focused on a development of a global structure in a family of distributed data realized on a basis of locally discovered structures. The local structures are revealed by running fuzzy clustering (Fuzzy C-Means), whereas building a global view is realized by forming global proximity matrices on a basis of the local proximity matrices implied by the partition matrices formed for the individual data sets. To capture the diversity of local structures, a global perspective at the structure of the data is captured in terms of a granular proximity matrix, which is built by invoking a principle of justifiable granularity with regard to the aggregation of individual proximity matrices. The three main scenarios are investigated: (a) designing a global structure among the data through building a granular proximity matrix, (b) refining a local structure (expressed in the form of a partition matrix) by engaging structural knowledge conveyed at the higher level of the hierarchy and provided in the form of the granular proximity matrix, (c) forming a consensus-building scheme and updating all local structures with the aid of the proximity dependences available at the upper layer of the hierarchy. While the first scenario delivers a passive approach to the development of the global structure, the two others are of an active nature by facilitating a structural feedback between the local and global level of the hierarchy of the developed structures. The study is illustrated through a series of experiments carried out for synthetic and publicly available data sets. [ABSTRACT FROM AUTHOR]

The study is concerned with a concept and a design of granular time series and granular classifiers. In contrast to the plethora of various models of time series, which are predominantly numeric, we propose to effectively exploit the idea of information granules in the description and classification of time series. The numeric (optimization-oriented) and interpretation abilities of granular time series and their classifiers are highlighted and quantified. A general topology of the granular classifier involving a formation of a granular feature space and the usage of the framework of relational structures (relational equations) in the realization of the classifiers is presented. A detailed design process is elaborated on along with a discussion of the pertinent optimization mechanisms. A series of experiments is covered leading to a quantitative assessment of the granular classifiers and their parametric analysis. [ABSTRACT FROM AUTHOR]

In spite of the truly remarkable diversity of models of time series, there is still an evident need to develop constructs whose accuracy and interpretability are carefully identified and reconciled subsequently leading to highly interpretable (human-centric) constructs. While a great deal of research has been devoted to the design of nonlinear numeric models of time series (with an evident objective to achieve high accuracy of prediction), an issue of interpretability (transparency) of models of time series becomes an evident and ongoing challenge. The user-friendliness of models of time series comes with an ability of humans to perceive and process abstract constructs rather than dealing with plain numeric entities. In perception of time series, information granules (which are regarded as realizations of interpretable entities) play a pivotal role. This gives rise to a concept of granular models of time series or granular time series, in brief. This study revisits generic concepts of information granules and elaborates on a fundamental way of forming information granules (both sets-intervals as well as fuzzy sets) through applying a principle of justifiable granularity encountered in granular computing. Information granules are discussed with regard to the granulation of time series in a certain predefined representation space (viz. a feature space) and granulation carried out in time. The granular representation and description of time series is then presented. We elaborate on the fundamental hierarchically organized layers of processing supporting the development and interpretation of granular time series, namely (a) formation of granular descriptors used in their visualization, (b) construction of linguistic descriptors used afterwards in the generation of (c) linguistic description of time series. The layer of the linguistic prediction models of time series exploiting the linguistic descriptors is outlined as well. A number of examples are offered throughout the entire paper with intent to illustrate the main functionalities of the essential layers of the granular models of time series. [ABSTRACT FROM AUTHOR]

People recognize familiar faces in a similar way by using interior facial features (facial regions) such as eyes, nose, mouth, etc. However, the importance of these regions in the realization of face identification and a quantification of the impact of such regions on the recognition process could vary from one region to another. An intuitively appealing observation is that of monotonicity: the more regions are taken into account in the recognition process, the better. From a formal point of view, the relevance of the facial regions and an aggregation of these pieces of experimental evidence can be described in the formal setting of fuzzy measures. Fuzzy measures are of particular interest with this regard given their monotonicity property (which stands in a clear contrast with the more restrictive additivity property inherent to probability-like measures). In this study, we concentrate on the construction of fuzzy measures (more specifically, $$ \lambda $$ -fuzzy measure) and characterize their performance in the problem of face recognition using a collection of experimental data. [ABSTRACT FROM AUTHOR]

This study provides a general introduction to the principles, algorithms and practice of computational intelligence (CI) and elaborates on those facets with relation to biomedical signal analysis, especially ECG signals. We discuss the main technologies of computational intelligence (namely, neural networks, fuzzy sets or granular computing, and evolutionary optimization), identify their focal points and stress an overall synergistic character, which ultimately gives rise to the highly symbiotic CI environment. Furthermore, the main advantages and limitations of the CI technologies are discussed. In the sequel, we present CI-oriented constructs in signal modeling, classification, and interpretation. Examples of the CI-based ECG signal processing problems are presented. [ABSTRACT FROM AUTHOR]

Clustering categorical data arising as an important problem of data mining has recently attracted much attention. In this paper, the problem of unsupervised dimensionality reduction for categorical data is first studied. Based on the theory of rough sets, the attributes of categorical data are decomposed into a number of rough subspaces. A novel clustering ensemble algorithm based on rough subspaces is then proposed to deal with categorical data. The algorithm employs some of rough subspaces with high quality to cluster the data and yields a robust and stable solution by exploiting the resulting partitions. We also introduce a cluster index to evaluate the solution of clustering algorithm for categorical data. Experimental results for selected UCI data sets show that the proposed method produces better results than those obtained by other methods when being evaluated in terms of cluster validity indexes. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the editor discusses various reports within the issue on topics including communication systems, networking for pervasive computing environments, and communication algorithms and protocols.

A correction to the article "Description, analysis, and classification of biomedical signals: a computational intelligence approach" that was published in the previous issue, is presented.

Image compression techniques realized in various ways have become an indispensable part in the practical storage and transmission of digital images. In this study, we present a novel method of lossy compression based on sampling and fuzzy encoding for grayscale images and discuss the problem of their reconstruction. First, an image is divided into a number of non-overlapping blocks of pixels. Next, we perform multiple rounds of random sampling. In each round, a number of pixels are selected as prototypes for the representing the corresponding block. Each pixel in the block is reconstructed based on the gray levels of the prototypes and membership degrees computed with respect to the distances of each pixel to the prototypes. The reconstruction abilities delivered by the prototypes are quantified by a certain objective fidelity criteria and the prototypes leading to lowest reconstruction error are determined as representatives of current block. Finally, once the representatives in each block have been determined, we reconstruct the whole image based on these prototypes. Experimental studies as well as visual evaluations show that the proposed algorithm is able to achieve high compression ratios while preserving the overall fidelity in the decompressed images. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the guest editors discuss various reports within the issue on topics including the research issues of the fusion of machine learning and cybernetics being cast in the framework of soft computing, the applications of machine learning and cybernetics to medical image processing and business intelligence, and the machine learning theories, technologies, and algorithms in soft computing.

Missing data are frequently encountered in reality, which inevitably poses great challenges to data mining techniques devoted to data structure identification. In view of the fact that missing data generally exhibits high uncertainty, this paper first introduces the concept of information granule, and performs granular imputation on missing data in a more abstract and inclusive way. With the tolerant nature of the information granule to uncertainty, the error of data imputation and the adverse effects on the subsequent research caused by the low-quality data can be largely reduced. Second, the initial data structure (including granular cluster centers and numeric partition matrix) of the data set with missing values is identified by performing fuzzy clustering on the mixed data set (including both numeric values and information granules) formed by imputation. Third, the bounds of granular cluster centers are further optimized by using the principle of justifiable granularity, and a more robust and reliable granular partition matrix is formed subsequently. Finally, by constructing a reconstruction criterion for mixed data, clustering performance and the optimization of some critical parameters (e.g., the cluster number) used in the proposed method could be investigated. This paper conducts comprehensive experimental studies on both synthetic and publicly available data sets to show the feasibility and effectiveness of the proposed data structure exploration method. [ABSTRACT FROM AUTHOR]

Humans maintain and develop interrelationships through various forms of communication, including verbal and nonverbal communications. Gestures, which constitute one of the most significant forms of nonverbal communication, convey meaning through diverse forms and movements across cultures. In recent decades, research efforts aimed at providing more natural, human-centered means of interacting with computers have garnered increasing interest. Technological advancements in real-time, vision-based hand motion recognition have become progressively suitable for human–computer interaction, aided by computer vision and pattern recognition techniques. Consequently, we propose an effective system for recognizing hand gestures using time-of-flight (ToF) cameras. The hand gesture recognition system outlined in the proposed method incorporates hand shape analysis, as well as robust fingertip and palm center detection. Furthermore, depth sensors, such as ToF cameras, enhance finger detection and hand gesture recognition performance, even in dark or complex backgrounds. Hand shape recognition is performed by comparing newly recognized hand gestures with pre-trained models using a YOLO algorithm-based convolutional neural network. The proposed hand gesture recognition system is implemented in real-world virtual reality applications, and its performance is evaluated based on detection performance and recognition rate outputs. Two distinct gesture recognition datasets, each emphasizing different aspects, were employed. The analysis of results and associated parameters was conducted to evaluate the performance and effectiveness. Experimental results demonstrate that the proposed system achieves competitive classification performance compared to conventional machine learning models evaluated on standard evaluation benchmarks. [ABSTRACT FROM AUTHOR]