A novel method to attribute reduction based on weighted neighborhood probabilistic rough sets

Abstract

Attribute reduction is an important application of rough set theory. Most existing rough set models do not consider the weight information of attributes in information systems. In this paper, we first study the weight of each attribute in information systems by using data binning method and information entropy theory. Then, we propose a new rough set model of weighted neighborhood probabilistic rough sets (WNPRSs) and investigate its basic properties. Meanwhile, the dependency degree formula of an attribute relative to an attribute subset is defined based on WNPRSs. Subsequently, we design a novel attribute reduction method by using WNPRSs and the corresponding algorithm is also given. Finally, to evaluate the performance of the proposed algorithm, we conduct a data experiment and compare it with other existing attribute reduction algorithms on eight public datasets. Experimental result demonstrates that the proposed attribute reduction algorithm is effective and performs better than some of the existing algorithms.