Accelerating ReliefF using information granulation

Wei Wei, Da Wang, Jiye Liang

Abstract: Feature selection is an essential preprocessing requirement when solving a classification problem. In this respect, the Relief algorithm and its derivatives have been demonstrated to be a class of successful feature selectors. However, the computational cost of these algorithms is very high when large-scale datasets are processed. To solve this problem, we propose the fast ReliefF algorithm based on the information granulation of instances (IG-FReliefF). The algorithm uses K-means to granulate the dataset and selects the significant granules among them using the criteria defined by information entropy and information granulation, and then evaluates each feature on the dataset composed of the selected granules. Extensive experiments show that the proposed algorithm is more efficient than the existing representative algorithms, especially on large-scale data sets, and the proposed algorithm is almost the same as the comparison algorithm in terms of classification performance.