中文摘要：

针对应用传统k近邻算法进行多标记文档分类时忽略了标记之间相关性的问题，提出了一种改进的ML—kNN多标记文档分类方法．针对文本特征的特点，采用一种基于KL散度的距离尺度来更好地描述文档相似度．根据近邻样本所属类别的统计信息，通过一种模糊最大化后验概率法则来推理未标记文档的标记集合．与ML—kNN不同的是，该方法可以有效地利用标记相关性来提升分类性能．在3个标准数据集上，5个多标记学习常用评测指标下的实验结果表明：所提方法在多标记文档分类问题上要明显优于ML—kNN、Rank，SVM和BoosTexter等主流多标记学习算法．

英文摘要：

Conventional kNN algorithms ignore label correlations when being applied to multi-label text categorization. To cover this shortage, an improved Multi-label kNN approach for text categorization is proposed. A specific distance metric based on KL divergence is derived to measure the similarity between individual documents. Based on statistical information gained from the label sets of neighboring documents, a fuzzy maximum a posteriori principle is utilized to conjecture the label sets of the unlabeled documents. Different from ML-kNN, the proposed approach can exploit label correlations to improve classification performance effectively. Experiments on three benchmark datasets using 5 popular multi-label evaluation metrics suggest that the proposed approach achieves superior performance to some well-established multi-label learning algorithms, such as ML-kNN, Rank-SVM and BoosTexter.