VISIT-based Evolutionary Approach for Fuzzy Classifier Design
Guangzhong Liu · Jisuanji gongcheng · 2007
An evolutionary approach for designing compact fuzzy classifier directly from data without any a priori knowledge of the data distribution is proposed.The variable input spread inference training(VISIT) algorithm is used to create each individual fuzzy system,and then searches the best one via genetic algorithm.Rules and membership functions are automatically created and optimized in an evolutionary process.In order to effectively evaluate the accuracy and compactness simultaneously,a fuzzy expert system acts as the fitness function.The experiments on two benchmark classification problems show the effectiveness of the new method.