An Efficient and Robust Feature Selection Approach Based on Zentropy Measure and Neighborhood-Aware Model

The feature selection based on the rough set (RS) theory has been an active research topic in data mining and knowledge discovery. Fuzzy RSs (FRSs), an efficient tool to process the inconsistency between features and decisions, have attracted attention to the problems of feature selection. However, most FRSs-based feature selection methods pay much attention to the approximation space while ignoring the interaction between different levels. Note that the single-level feature selection method, depending on the boundary objects, is easily influenced by the noise data and cannot integrate multiple granular levels to evaluate features accurately. Therefore, this article proposes an efficient and robust feature selection approach based on the neighborhood-aware model and zentropy measure. Specifically, we first define a neighborhood-aware FRS (NAFRS) with weighted fuzzy relation to improve the antinoise ability of FRSs. Then, we propose a fuzzy granule zentropy (FGZE) measure based on zentropy by analyzing the granular level relation in NAFRS. Moreover, a significance measure with FGZE is designed and applied to feature selection. Finally, the experimental results of our method on 22 datasets by comparing it with 12 representative feature selection methods demonstrate the antinoise and the classification ability of the proposed method.