An efficient selector for multi-granularity attribute reduction

Abstract Presently, the mechanism of multi-granularity has been frequently realized by various mathematical tools in Granular Computing especially rough set. Nevertheless, as a key topic of rough set, attribute reduction has been rarely exploited by the concept of multi-granularity. To fill such a gap, Multi-Granularity Attribute Reduction is defined to characterize reduct which satisfies the intended multi-granularity constraint instead of one and only one granularity based constraint. Furthermore, to accelerate the searching process of reduct, Multi-Granularity Attribute Selector is introduced into the framework of heuristic algorithm. Its key procedure is twofold including: (1) fuse all the granularities based measure-values to construct the multi-granularity constraint; (2) integrate the suitable granularities based measure-values to evaluate the candidate attributes. Based on the multi-granularity structure formed by neighborhood rough set, the experimental results over 20 UCI data sets demonstrate that compared with single granularity attribute reduction, our selector can not only generate reducts which may not contribute to poorer classification performances, but also significantly reduce the elapsed time of computing reducts. This research suggests the new trend of attribute reduction in multi-granularity environment.