Multi-label Feature Selection Method via Maximizing Correlation-based Criterion with Mutation Binary Bat Algorithm
Yuanyuan Tao, Jun Li, Jianhua Xu · 2020
Multi-label feature selection is a vital pre-processing step to reduce computational complexity, improve classification performance and enhance model interpretability, via selecting a discriminative subset of features from original high-dimensional features. Correlation-based feature selection (CFS) criterion measures the relevance between features and labels, and the redundancy among features, which has been combined with hill climbing and genetic algorithm to execute multi-label feature selection task. However, it is an open problem to search for more effective optimization tools for CFS. In this paper, through adding a mutation operation, we modify existing binary bat algorithm to build its mutation version (MBBA), to adjust the number of "1" components to be a fix size. Then a new multi-label feature selection approach is proposed via maximizing CFS criterion using MBBA, to select a fixed number of discriminative features. Our experiments on four data sets show that our proposed method is superior to three state-of-the-art approaches, according to four sample-based performance evaluation metrics for multi-label classification.