Efficient and interpretable fuzzy classifiers from data with support vector learning

Article type: Research Article

Authors: Papadimitriou, Stergios^{; *} | Terzidis, Konstantinos

Affiliations: Technological Educational Institute of Kavala, Department of Information Management, Kavala, 65404, Greece. E-mail: [email protected], [email protected]

Correspondence: [*] Corresponding author.

Abstract: 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).

Keywords: Support vector machines, interpretable rules, fuzzy rules, rule mining, kernel classifiers, fuzzy basis functions

DOI: 10.3233/IDA-2005-9603

Journal: Intelligent Data Analysis, vol. 9, no. 6, pp. 527-550, 2005