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基于决策粗糙集的多标记情绪分类*

DOI: 10.16451/j.cnki.issn1003-6059.201508002, PP. 680-685

Keywords: 情绪分类,多标记学习,粗糙集,决策粗糙集

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Abstract:

为解决情绪分类中的多标记不确定性问题,提出基于决策粗糙集的多标记分类方法(DTRS-MLC).该方法利用双加权多标记K近邻算法(DW-ML-KNN)多标记的实值函数定义正域、负域和边界域,通过标记依赖度统一刻画标记的共现和互斥关系.从理论和实验角度分析DTRS-MLC与DW-ML-KNN的关系,验证DW-ML-KNN属于DTRS-MLC的特例.音乐情绪分类及文本情绪分类的实验均表明,DTRS-MLC在整体上取得较好性能.

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