基于稳健模糊粗糙集模型的多标记文本分类

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。

Multi-label Text Classification Based on Robust Fuzzy Rough Set Model

Owing to the uncertainty of multi-label data and noise data,a novel multi-label robust fuzzy rough classification model was proposed,which is an extension of k-mean robust statistics fuzzy rough classification model that is used to solve the single label classification problem.First,for each unlabeled instance,the membership with respect to each label was obtained by similarity measures.Second,according to the membership,the degree of correlation was defined.Finally,an appropriate threshold was given to demarcate the correlated and uncorrelated labels. The experimental results on three benchmark multi-label datasets and one actual multi-label datasets indicate that the proposed model is superior to ML-kNN and rank-SVM across six popular multi-label evaluation metrics.