Forward tentative selection with backward propagation of selection decision algorithm for attribute reduction in rough set theory Online publication date: Mon, 09-Nov-2015
by Srilatha Chebrolu; Sriram G. Sanjeevi
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 7, No. 3/4, 2015
Abstract: In rough set theory, attribute reduction is an important application. Many approaches to attribute reduction were developed using rough set theory. These approaches used various greedy heuristics to find a reduct approximation. In this paper, we propose forward tentative selection with backward propagation of selection decision (FTSBPSD) algorithm to find a reduct. The proposed algorithm is based on the principle of indiscernibility of rough set theory. It finds one of the prime implicants of the discernibility function as the reduct. The proposed algorithm works for various types of reducts, defined in the rough set theory. In this work, we have analysed the performance of distribution reduct, maximum distribution reduct, positive region reduct and possible reduct. The proposed algorithm was tested on various datasets found in University of California, machine learning repository. It has given good results for classification accuracy during tests performed on the datasets. Experimental results obtained by FTSBPSD algorithm have been found to give better classification accuracy when tested using C4.5 classifier in comparison to the results obtained by the Q-MDRA algorithm described in the literature.
Online publication date: Mon, 09-Nov-2015
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