Approximating mutual information for multi-label feature selection

Abstract

Proposed is a new multi-label feature selection method that captures relationships between features and labels without transforming the problem into single-label classification. Using approximated joint mutual information, the proposed incremental feature selection algorithm provides markedly better classification performance than well-known conventional methods.