关键词: 多标签分类, 标签关联信息, 概率模型, 交替最大化算法

Abstract: Multi-label classification methods have been applied in many real-world fields. To the problem of label-relevance in multi-label classification, this paper proposes a novel multi-label classification method by automatically acquiring label relevant information. The method introduces a probabilistic model to solve the established optimization sub-problem by using alternative maximization algorithm. It also gives the inference. This model can acquire label relevant information, and it can achieve better multi-label classification results. The experimental results on four real multi-label datasets show that the proposed method can achieve higher classification and prediction evaluation values and several other measure index values than the existing multi-label classification methods.

Key words: multi-label classification, label relevant information, probabilistic model, alternative maximization algorithm