Feature Selection Based on Neighborhood Systems and Rough Set Theory
Ming He · 2009
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.