A neuro-fuzzy classification technique using dynamic clustering and GSS rule generation

An efficient feature subset selection for predictive and accurate classification is highly desirable in many application domains like medical diagnosis, target marketing etc. Many neuro-fuzzy models were proposed for feature selection and efficient classification. One of such existing neuro-fuzzy models is Enhance Neuro-Fuzzy (ENF) system for classification using dynamic clustering. The major problem of ENF is, huge number of linguistic variables generated for each feature, which results in poor interpretation of the rules generated for classification. Therefore, this paper proposes a neuro-fuzzy model which is an extension of ENF. The novelty of the proposed model lies in determining less number of linguistic variables for each feature and also in generating significant linguistic variables in the rules for classification with better interpretation and accuracy. Six datasets are used to test the performance of the proposed model. 10-fold cross validation is used to compare the performance of the proposed model with others. It is observed from the experimental results that the performance of the proposed model is superior to others.