A Fast Attribute Reduction Algorithm Based on a Positive Region Sort Ascending Decision Table

Abstract

Attribute reduction is one of the challenging problems in rough set theory. To accomplish an efficient reduction algorithm, this paper analyzes the shortcomings of the traditional methods based on attribute significance, and suggests a novel reduction way where the traditional attribute significance calculation is replaced by a special core attribute calculation. A decision table called the positive region sort ascending decision table (PR-SADT) is defined to optimize some key steps of the novel reduction method, including the special core attribute calculation, positive region calculation, etc. On this basis, a fast reduction algorithm is presented to obtain a complete positive region reduct. Experimental tests demonstrate that the novel reduction algorithm achieves obviously high computational efficiency.

Indistinguishable Element-Pair Attribute Reduction and Its Incremental Approach

“…For the convenience of the following description, Table 1 summarizes the list of abbreviations in the article. In the last two decades, many heuristic attribute reduction approaches have been developed based on the positive region [9], discernibility matrix [10,11], information entropy [12], fuzzy rough [13,14], m-polar fuzzy [15,16], and knowledge granularity [17].…”