HYEI: A New Hybrid Evolutionary Imperialist Competitive Algorithm for Fuzzy Knowledge Discovery

Abstract

In recent years, imperialist competitive algorithm (ICA), genetic algorithm (GA), and hybrid fuzzy classification systems have been successfully and effectively employed for classification tasks of data mining. Due to overcoming the gaps related to ineffectiveness of current algorithms for analysing high-dimension independent datasets, a new hybrid approach, named HYEI, is presented to discover generic rule-based systems in this paper. This proposed approach consists of three stages and combines an evolutionary-based fuzzy system with two ICA procedures to generate high-quality fuzzy-classification rules. Initially, the best feature subset is selected by using the embedded ICA feature selection, and then these features are used to generate basic fuzzy-classification rules. Finally, all rules are optimized by using an ICA algorithm to reduce their length or to eliminate some of them. The performance of HYEI has been evaluated by using several benchmark datasets from the UCI machine learning repository. The classification accuracy attained by the proposed algorithm has the highest classification accuracy in 6 out of the 7 dataset problems and is comparative to the classification accuracy of the 5 other test problems, as compared to the best results previously published.