A novel variable precision rough set attribute reduction algorithm based on local attribute significance

Abstract

In attribute reduction, the method of partitioning information granules is crucial; however, the existing attribute reduction algorithms have limitations in this respect. To solve this problem, this study proposes the concept of an (α,β)-indiscernibility relation. This is a fresh partition approach for information granules, tolerating a certain degree of error information. On the basis of this concept, we propose a novel (α,β)-rough set model. Then we use this model to design a novel attribute reduction algorithm based on local attribute significance. To maintain the objectivity of this research and ensure fairness of the experiments, we present an integrated classifier to achieve the greatest classification accuracy. Ultimately, the experimental results verify that the fault-tolerance ability of information granules is increased. Moreover, the novel attribute reduction algorithm deletes redundant attributes and noise attributes to increase classification accuracy. In the later part of the experiment, different proportions of random Gaussian noise are added to each data set. The experimental results demonstrate the robustness and superiority of the new algorithm.