Multilabel feature selection is an effective preprocessing step for improving multilabel classification accuracy, because it highlights discriminative features for multiple labels. Recently, multi-population genetic algorithms have gained significant attention with regard to feature selection studies. This is owing to their enhanced search capability when compared to that of traditional genetic algorithms that are based on communication among multiple populations. However, conventional methods employ a simple communication process without adapting it to the multilabel feature selection problem, which results in poor-quality final solutions. In this paper, we propose a new multi-population genetic algorithm, based on a novel communication process, which is specialized for the multilabel feature selection problem. Our experimental results on 17 multilabel datasets demonstrate that the proposed method is superior to other multi-population-based feature selection methods.

Multi-label feature selection is an important task for text categorization. This is because it enables learning algorithms to focus on essential features that foreshadow relevant categories, thereby improving the accuracy of text categorization. Recent studies have considered the hybridization of evolutionary feature wrappers and filters to enhance the evolutionary search process. However, the relative effectiveness of feature subset searches of evolutionary and feature filter operators has not been considered. This results in degenerated final feature subsets. In this paper, we propose a novel hybridization approach based on competition between the operators. This enables the proposed algorithm to apply each operator selectively and modify the feature subset according to its relative effectiveness, unlike conventional methods. The experimental results on 16 text datasets verify that the proposed method is superior to conventional methods.

Accurate music tag classification of music clips has been attracting great attention recently, because it allows one to provide various music excerpts, including unpopular ones, to users based on the clips’ acoustic similarities. Given a user’s preferred music, acoustic features are extracted and then fed into the classifier, which outputs the related tag to recommend new music. Furthermore, the accuracy of the tag classifiers can be improved by selecting the best feature subset based on the domain to which the tag belongs. However, recent studies have struggled to evaluate the superiority of various classifiers because they utilize different feature extractors. In this study, to conduct a direct comparison of existing methods of classification, we create 20 music datasets with the same acoustic feature structure. In addition, we propose an effective evolutionary feature selection algorithm to evaluate the effectiveness of feature selection for tag classification. Our experiments demonstrate that the proposed method improves the accuracy of tag classification, and the analysis with multiple datasets provides valuable insights, such as the important features for general music tag classification in target domains.