计算机科学 ›› 2015, Vol. 42 ›› Issue (7): 270-275.doi: 10.11896/j.issn.1002-137X.2015.07.058
张 晶,李德玉,王素格,李 华
ZHANG Jing, LI De-yu, WANG Su-ge and LI Hua
摘要： 针对多标记数据的不确定性以及噪声数据的存在,提出了一种新的多标记稳健模糊粗糙分类模型。该模型是处理单标记分类问题的k-mean稳健统计量模糊粗糙分类模型的扩展应用。对于每个待分类数据,首先根据相似性计算方法,得到它们相对于各标记的隶属度；然后根据隶属度定义待分类数据与各标记的相关度；最后为每一组相关度赋予合适的阈值,得到相关的标记集合。在3个标准多标记数据集和1个真实多标记文本数据集上的实验结果表明,对于多标记文本分类问题,所提模型在 6个常用的多标记评测指标上较常用的ML-kNN和rank-SVM多标记学习方法具有更高的准确率。
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