Efficient Symbolic and Numerical Attribute Reduction with Neighborhood Rough Sets

Rough set theory is widely used in attribute reduction.Computational complexity is one of the factors to limit applicability in reduction techniques,especially in the neighborhood rough set based reduction. In this paper,some mathematical properties of neighborhood rough set model are analyzed.An efficient method is proposed for forward attribute selection strategy based on dependency by using the property that positive region monotonously increases with the amount of attributes.By this algorithm,the comparison times of the samples in computing positive region and neighborhood are reduced,and thus the computational efficiency is improved.The experimental results show that the proposed method is effective.