Bidirectional heuristic attribute reduction based on conflict region

Abstract

Attribute reduction is one of the key issues in rough set theory. Many heuristic reduction strategies such as forward heuristic reduction, backward heuristic reduction and for-backward heuristic reduction have been proposed to obtain a subset of attributes which has the same discernibility as the original attribute set. However, some methods are usually computationally time consuming for large data sets. Therefore, this paper focuses on solving the attribute reduction efficiency in the decision system. We first introduce the quotient of approximation, positive region and conflict region, and then research the heuristic reduction algorithm based on conflict region. Sequentially, we put forward to a mechanism of bidirectional heuristic attribute reduction based on conflict region quotient and design a bidirectional heuristic attribute reduction algorithm. Finally, the experimental results with UCI data sets show that the proposed reduction algorithm is an effective technique to deal with large high-dimensional data sets.

Incremental Attribute Reduction Under Variations of the Attribute Set Based on Conflict Region

“…The quotient sets of the positive, boundary and negative regions of X with respect to R are defined as follows, respectively. [35] QPOS…”

“…and R  C, the conflict region and the quotient set of conflict region of D with respect to R are defined as follows, respectively. [35] CON…”

“…and R  C, for aC-R, the joined significance measure of the attribute a regarding to R can be defined as. [35] Sig…”

“…and R  C, for aR, the weeded significance measure of the attribute a regarding to R can be defined as. [35] Sig and R  C, R is the reduct of positive region, which satisfies with two conditions as follows.…”