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| Multi-label Emotion Classification Based on Decision-Theoretic Rough Set |
| ZHANG Zhi-Fei, MIAO Duo-Qian, ZHANG Hong-Yun |
| Department of Computer Science and Technology, Tongji University, Shanghai 201804 |
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Abstract To solve the problem of multi-lable uncertainty in emotion classification, a multi-label classification method based on decision-theoretic rough set, named DTRS-MLC, is proposed. The positive, negative, and boundary regions with the multi-label mapping function are defined by the dual-weighted multi-label K-nearest neighbor (DW-ML-KNN) algorithm, and the label co-occurrence and label exclusiveness relationship with the label dependency degree is described. From the perspective of theoretical and experimental analysis of the relationship between DTRS-MLC and DW-ML-KNN, DW-ML-KNN can be viewed as a special case of DTRS-MLC. The experimental results on music and text emotion classification tasks show that DTRS-MLC achieves better performance as a whole.
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Received: 13 June 2014
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2015, Vol. 28 
