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A Rough Set Approach to Feature Selection Based on Wasp Swarm Optimization

As an important concept of rough set theory, an attribute reduction is a subset of attributes that are jointly sufficient and individually necessary for preserving a particular property of the given information table. In order to acquire minimal attribute reduction, we propose a wasp swarm optimization algorithm for attribute reduction based on rough set and the significance of feature. The significance of feature is constructed based on the mutual information between selected conditional attributes and decisional attributes. The algorithm dynamically calculates heuristic information based on the significance of feature to guide search. Experimental are carried out on some standard UCI datasets. The results demonstrate that, in terms of solution quality and computational effort, proposed algorithm can get better results than other intelligent swarm algorithms for attribute reduction.