Multi-Label Feature Selection Based on Improved Fisher Score with Label Correlation

Feature selection for multi-label classification has received extensive attention in the fields of machine learning and data mining. However, some feature selection methods fail to incorporate label correlations, resulting in the degradation of learning performance. In this paper, we dedicate to fuse the Fisher score model and neighborhood mutual information model for multi-label learning. The neighborhood mutual information is initially constructed to identify the coefficients between labels, by measuring the granularity of each instance. After that, an improved Fisher score model based on label coefficients is formed to select features with multiple crucial labels. Extensive experiments demonstrate the effectiveness and stability of the proposed method.