Attribute reduction on real-valued data in rough set theory using hybrid artificial bee colony: extended FTSBPSD algorithm

Abstract

Discretization and attribute reduction are two preprocessing steps for most of the induction algorithms. Discretization before attribute reduction will result in high computation cost as many irrelevant and redundant attributes need to be discretized. Attribute reduction before discretization may result in over-fitting of the data leading to low performance of the induction algorithm. In this paper, we have proposed a hybrid algorithm using artificial bee colony (ABC) algorithm and extended forward tentative selection with backward propagation of selection decision (EFTSBPSD) algorithm for attribute reduction on real-valued data in rough set theory (RST). Based on the principle of indiscernibility, the hybrid ABC–EFTSBPSD algorithm performs discretization and attribute reduction together. The hybrid ABC–EFTSBPSD algorithm takes as input the decision system consisting of real-valued attributes and determines a near optimal set of irreducible cuts. Here, optimality of the set of irreducible cuts is defined in terms of the cardinality of the set of irreducible cuts. Reduct is obtained from the determined approximate optimal set of irreducible cuts by extracting the attributes corresponding to the cuts in the obtained set of irreducible cuts. The proposed hybrid algorithm is tested on various data sets from University of California Machine Learning Repository. Experimental results obtained by the proposed hybrid algorithm are compared with those obtained by the Q-MDRA, ACO-RST and IMCVR algorithms described in the literature and found to give better classification accuracy when tested using (1) C4.5 classifier and (2) SVM classifier. The proposed hybrid algorithm has also shown reduced length of the reduct in comparison with the results obtained by Q-MDRA, ACO-RST and IMCVR algorithms.