Journal
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
Volume 103, Issue -, Pages 394-413Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2018.10.009
Keywords
Multi-label classification; Three-way decisions; Selective ensemble; Uncertainty; Probabilistic rough set
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Funding
- National Key R&D Program of China [213]
- National Science Foundation of China [61673301, 61763031, 61563016]
- Major Project of Ministry of Public Security [20170004]
- Open Research Funds of State Key Laboratory for Novel Software Technology [KEKT2017B22]
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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. (C) 2018 Elsevier Inc. All rights reserved.
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