Neural network implementation of fuzzy logic
Fuzzy Sets and Systems
Machine Learning
Fuzzy engineering
Generalization performance of support vector machines and other pattern classifiers
Advances in kernel methods
Making large-scale support vector machine learning practical
Advances in kernel methods
Support vector machines for dynamic reconstruction of a chaotic system
Advances in kernel methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Extracting Interpretable Fuzzy Rules from RBF Networks
Neural Processing Letters
Neural Computation
Growing kernel-based self-organized maps trained with supervised bias
Intelligent Data Analysis
Symbolic adaptive neuro-fuzzy inference for data mining of heterogenous data
Intelligent Data Analysis
Fuzzy function approximation with ellipsoidal rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Support vector learning for fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
IEEE Transactions on Fuzzy Systems
Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms
IEEE Transactions on Fuzzy Systems
Generating an interpretable family of fuzzy partitions from data
IEEE Transactions on Fuzzy Systems
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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The construction of fuzzy rule-based classification systems with both good generalization ability and interpretability is a chalenging issue. The paper aims to present a novel framework for the realization of these important (and many times conflicting) goals simultaneously. The generalization performance is obtained with the adaptation of Support Vector algorithms for the identification of a Support Vector Fuzzy Inference (SVFI) system. The SVFI is a fuzzy inference system that implements the Support Vector network inference and inherits from it the robust learning potential. The construction of the SVFI is based on the algorithms presented in [15]. The contribution of the paper is the development of algorithms for the construction of interpretable rule systems on top of the SVFI system. However, the SVFI rules usually lack interpretability. For this reason, the accurate set of rules can be approximated with a simpler interpretable fuzzy system that can present insight to the more important aspects of the data. Therefore, the presented approach utilizes two sets of fuzzy rules: the accurate Support Vector Fuzzy Inference (SVFI) rules and the approximate interpretable one that is derived from the SVFI with a set of tunable threshold parameters. The paper initially reviews the peculiarities of extracting fuzzy rules with Support Vector learning for classification problems. The interpretable fuzzy system construction algorithms receive an a priori description of a set of fuzzy sets that describe the linguistic aspects of the input variables as they are usually perceived by the human experts. After this specification the presented algorithms extract from the SVFI rules a small and concise set of interpretable rules. We apply our method to both synthetic data, data sets from the UCI repository and real gene expression data and we demonstrate its efficiency in uncovering clear and concise rules in application domains where interpretable linguistic labels can be pre-assigned (as in gene expression analysis).