A bi-phased multi-objective genetic algorithm based classifier. (15th May 2020) - Dutta, Dipankar; Sil, Jaya; Dutta, Paramartha
Highlights: We have proposed a Bi-Phased Multi-Objective Genetic Algorithm. We have done data classification by the proposed algorithm. It is a cyclic algorithm. Statistical tests show that the performance of the proposed algorithm is comparable. Abstract: This paper presents a novel Bi-Phased Multi-Objective Genetic Algorithm ( BPMOGA ) based classification method. It is a Learning Classifier System ( LCS ) designed for supervised learning tasks. Here we have used Genetic Algorithms ( GA s) to discover optimal classifiers from data sets. The objective of the work is to find out a classifier or Complete Rule ( CR ) which comprises of several Class Specific Rules ( CSR s). Phase-I of BPMOGA extracts optimized CSR s in I F − T H E N form by following Michigan approach, without considering interaction among the rules. Phase-II of BPMOGA builds optimized CR s from CSR s by following Pittsburgh way. It combines the advantages of both approaches. Extracted CR s help to build CSR s for the next run of phase-I. Hence, phase-I and phase-II are cyclically related, which is one of the uniqueness of BPMOGA . With the help of twenty one benchmark data sets from the University of California at Irvine ( UCI ) machine learning repository we have compared performance of BPMOGA based classifier with fourteen GA and non- GA based classifiers. Statistical test shows that the performance of the proposed classifier is either superior or comparable to other classifiers.