一种适应于非完备标签数据和标签关联性的多标签分类方法

A multi-label classification method for disposing incomplete labeled data and label relevance

多标签分类已在很多领域得到了实际应用,所用标签大多具有很强的关联性,甚至存在非完备标签或部分标签遗失。然而,现有的多标签分类算法难以同时处理这两种情况。基于此,提出一种新的概率模型处理方法,实现同时对具有标签关联性和遗失标签情况进行多标签分类。该方法可以自动获知和掌握多标签的关联性。此外,通过整合遗失的标签信息,该方法能够提供一个自适应策略来处理遗失的标签。在完备标签和非完备标签的数据上进行实验,结果表明,与现有的多标签分类算法相比,提出的方法得到了较好的分类预测评价值。

Multi-label classification methods have been applied in many real-world fields, in which the labels may have strong relevance and some of them even are incomplete or missing. However, existing multi-label classification algorithms are unable to handle both issues simultaneously. A new probabilistic model that can automatically learn and exploit multi-label relevance was proposed on label relevance and missing label classification simultaneously. By integrating out the missing information, it also provides a disciplined approach to handle missing labels. Experiments on a number of real world data sets with both complete and incomplete labels demonstrated that the proposed method can achieve higher classification and prediction evaluation scores than the existing multi-label classification algorithms.