The Improvement of Attribute Reduction Algorithm Based on Information Gain Ratio in Rough Set Theory

Abstract

Due to the various data sets and the cumbersome and diverse data types, there must be many redundant attributes in them, which greatly increases the classification time in the background of rough set theory. In this paper, we improve the attribute reduction algorithm by information gain ratio. The data sets obtained after the attribute reduction of this method are used for classification, and the data sets are directly used for classification and comparison with other common classification methods. Experimental data verify that the improved algorithm in this article is effective. It can improve the classification speed greatly and shorten the time spent.

A New Approach for Attribute Reduction from Decision Table based on Intuitionistic Fuzzy Topology

“…There are two general feature selection strategies: wrappers [36] and filters [35]. While the wrapper strategy employs learning algorithms to evaluate selected attribute subsets, the Filter strategy selects attributes based on some measures such as information gain [24,26,27,28,29,33,34,39,40], consistency [1,23,25,30,47], distance [8,9,35,42,43], and dependency [27, 41,38,46]. These measures can be classified into distance and positive regions [30].…”