A three-way selective ensemble model for multi-label classification

Abstract Label ambiguity and data complexity are widely recognized as major challenges in multi-label classification. Existing studies strive to find approximate representations concerning label semantics, however, most of them are predefined, neglecting the personality of instance-label pair. To circumvent this drawback, this paper proposes a three-way selective ensemble (TSEN) model. In this model, three-way decisions is responsible for minimizing uncertainty, whereas ensemble learning is in charge of optimizing label associations. Both label ambiguity and data complexity are firstly reduced, which is realized by a modified probabilistic rough set. For reductions with shared attributes, we further promote the prediction performance by an ensemble strategy. The components in base classifiers are label-specific, and the voting results of instance-based level are utilized for tri-partition. Positive and negative decisions are determined directly, whereas the deferment region is determined by label-specific reduction. Empirical studies on a collection of benchmarks demonstrate that TSEN achieves competitive performance against state-of-the-art multi-label classification algorithms.