基于决策粗糙集的多标记情绪分类  

Multi-label Emotion Classification Based on Decision-Theoretic Rough Set

作　　者：张志飞[1] 苗夺谦[1] 张红云[1] 

机构地区：[1]同济大学计算机科学与技术系,上海201804

出　　处：《模式识别与人工智能》2015年第8期680-685,共6页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61273304;61202170);高等学校博士学科点专项科研基金项目(No.20130072130004);上海市自然科学基金项目(No.14ZR1442600)资助

摘　　要：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.To solve the problem of muhi-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.

关 键 词：情绪分类 多标记学习 粗糙集 决策粗糙集 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.1[自动化与计算机技术—控制科学与工程]