Feature Selection and Identification of Fuzzy Classifiers Based on the Cuckoo Search Algorithm

Abstract

Classification is an important problem of data mining. The main advantage of fuzzy methods for extracting classification rules from empirical data is that the user can easily understand and interpret these rules, which makes fuzzy classifiers a useful modeling tool. A fuzzy classifier uses IF-THEN rules, with fuzzy antecedents (IF-part of the rule) and class labels in consequents (THEN-part of the rule). A method to constructing fuzzy classifiers based on the cuckoo search metaheuristic is described. The proposed method to constructing fuzzy classifiers based on observations data involves three stages: (1) feature selection, (2) structure generation, and (3) parameter optimization. The contributions of this paper are: (i) proposal of Cuckoo Search based feature selection; (ii) proposal of Cuckoo Search based parameter optimization of fuzzy classifier; (iii) proposal of subtractive clustering algorithm for structure generation of fuzzy classifier; and (iv) experiments with well-known benchmark classification problems (wine, vehicle, hepatitis, segment, ring, twonorm, thyroid, spambase reproduction data sets).