Attribute Reduction of Boolean Matrix in Neighborhood Rough Set Model

Neighborhood rough set is a powerful tool to deal with continuous value information systems. Graphics processing unit (GPU) computing can efficiently accelerate the calculation of the attribute reduction and approximation sets based on matrix. In this paper, we rewrite neighborhood approximation sets in the matrix-based form. Based on the matrix-based neighborhood approximation sets, we propose the relative dependency degree of attributes and the corresponding algorithm (DBM). Furthermore, we design the reduction algorithm (ARNI) for continuous value information systems. Compared with other algorithms, ARNI can effectively remove redundant attributes, and less affect the classification accuracy. On the other hand, the experiment shows ARNI based on the matrixing rough set model can significantly speed up by GPU. The speedup is many times over the central processing unit implementation.

Abstract licence: CC BY-NC-ND

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