Multi-Label Feature Selection With Fuzzy Rough Sets

Feature selection for multi-label classification tasks has attracted attention from the machine learning domain. The current algorithms transform a multi-label learning task to several binary single-label tasks, and then compute the average score of the features across all single-label tasks. Few research discusses the effect in averaging the scores. To this end, we discuss multi-label feature selection in the framework of fuzzy rough sets. We define a novel dependency functions with three fusion methods if the fuzzy lower approximation of each label has been calculated. A forward greedy algorithm is constructed to reduce the redundancy of the selected features. Numerical experiments validate the performance of the proposed method.