Granular structure-based incremental updating for multi-label classification

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Incremental learning is an efficient computational paradigm of acquiring approximate knowledge of data in dynamic environment. Most of the research focuses on knowledge updating for single-label classification, whereas incremental mechanism for multi-label classification is of preliminary nature. This leads to considerable computation complexity to maintain desired performance. To address this challenge, we formulate a granular structure system (GSS). The proposed granular structure system in bottom-up way provides a systematic view on label-specific based classification. We demonstrate that the three-way selective ensemble (TSEN) model, a state-of-the-art solution for multi-label classification, is compatible with GSS in granulation. An incremental mechanism of GSS is introduced for both label-specific feature generation and optimization, and an incremental three-way selective ensemble algorithm for multiple instances immigration (IMOTSEN) is presented. Experiments completed on six datasets show that the proposed algorithm can maintain considerable classification performance while significantly accelerating the knowledge (GSS) updating.