Granular structure-based incremental updating for multi-label classification.

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 (G S S). 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 (T S E N) model, a state-of-the-art solution for multi-label classification, is compatible with G S S in granulation. An incremental mechanism of G S S is introduced for both label-specific feature generation and optimization, and an incremental three-way selective ensemble algorithm for multiple instances immigration (I M O T S E N) is presented. Experiments completed on six datasets show that the proposed algorithm can maintain considerable classification performance while significantly accelerating the knowledge (G S S) updating. • Formulate a granular structure system for multi-label classification. • Present an incremental mechanism for multi-label based on proposed structure. • Demonstrate the efficiency and effectiveness of proposed algorithm. [ABSTRACT FROM AUTHOR]