Attribute subset selection via neighborhood composite entropy-based fuzzy β-covering

Attention has been widely paid to fuzzy β-covering, a powerful tool for processing uncertain information, and has recently turned into a hot topic. However, the ubiquity of unbalanced data makes fuzzy β-covering models are unavailable since they ignore the decision distribution normally. To address this drawback, a novel fuzzy β-covering attribute subset selection based on neighborhood composite entropy is proposed in this paper. First, the similarity measure of objects is discussed based on fuzzy β-neighborhoods for unbalanced fuzzy data. Subsequently, a neighborhood composite entropy is developed, which fully considers the decision distribution. Furthermore, an algorithm for attribute selection is presented according to the framework of the above measure mechanism. Furthermore, experimental results on 12 reality data from UCI and KRBM show that this novel method performs better classification on the proposed model to prove the performance and effectiveness by comparing several existing models.