Feature Selection Based on Neighborhood Systems and Rough Set Theory

Attribute reduction is an important issue in data mining and knowledge acquisition. It has been proven that computing all reductions and optimal (minimal) reduction is a NP-hard problem. This paper proposed a hybrid approach using the rough set theory and neighborhood systems for feature selection. Two neighborhood approximation operators are defined based on rough set. A neighborhood rough model is constructed subsequently and the heuristic information is introduced according to the significance of attributes respectively. Experimental results indicate that the proposed method can reduce attributes effectively.