Multi-label feature selection based on correlation label enhancement

Abstract

Feature selection is an effective data preprocessing technique that can effectively alleviate the curse of dimensionality in multi-label learning. The technique selects a subset of features with high discriminative power to maintain or improve the classifier's classification performance. In many practical applications, it is not possible to use multi-label data to express the relative importance of each label versus each sample. Therefore, all relevant or irrelevant labels are measured with the same importance, neglecting the potential relationship between labels and samples. To better account for the relationship between samples and labels, traditional logical labels are enhanced to label distributions. Aiming at the application of latent sample correlation in label enhancement, a new label enhancement algorithm is proposed. First, to extract potential correlation from the sample space, an objective function is proposed and solved. Second, in the process of label enhancement, the correlation is embedded into label distributions. Finally, label distributions are applied to the sparse linear regression model instead of logical labels. Experiments show that proposed method is superior to 5 advanced multi-label feature selection algorithms on 11 multi-label datasets.