Online Multilabel Streaming Feature Selection by Label Enhancement and Fuzzy Synergistic Discrimination Information

Online streaming feature selection is an effective approach for handling large-scale streaming data in real-world applications. However, many existing online streaming feature selection studies do not effectively leverage the correlation and hidden information in the label space. The measures used in streaming feature selection often face challenges such as computational complexity and insufficient information exploitation. To address these issues, we propose an online streaming feature selection method based on label enhancement and fuzzy synergistic discrimination information. First, we define a novel label enhancement method based on label correlation, which does not require any prior knowledge of feature space. Second, we propose a new measure to evaluate the discriminative ability of multiple feature subsets for labels in fuzzy environment, called fuzzy synergistic discrimination information. It is bounded and has many interesting properties. The fuzzy synergistic discrimination information is computed directly based on fuzzy similarity relations, so the calculation is relatively simple. Third, based on label enhancement and proposed fuzzy synergistic discrimination information, we construct a three-stage online multilabel streaming feature selection method, including online significance analysis, online relevance analysis, and online redundancy analysis. Finally, comparative experiments on 12 datasets demonstrate the effectiveness and superiority of the proposed method. The influences of different feature sequences and these components on the algorithm's performance are further analyzed.