Multi-view multi-label active learning with conditional Bernoulli mixtures

Multi-label classification is very common in practical applications. Compared with multi-class classification, multi-label classification has larger label space and thus the annotations of multi-label instances are typically more time-consuming. It is significant to develop active learning methods for multi-label classification problems. In addition, multi-view learning is more and more popular, which treats data from different views discriminatively and integrates information from all the views effectively. Introducing multi-view methods into active learning can further enhance its performance when processing multi-view data. In this paper, we propose multi-view active learning methods for multi-label classifications. The proposed methods are developed based on the conditional Bernoulli mixture model which is an effective model for multi-label classification. For making active selection criteria, we consider selecting informative and representative instances. From the informative perspective, least confidence and entropy of the predicting results are employed. From the representative perspective, clustering results on the unlabeled data are exploited. Particularly for multi-view active learning, novel multi-view prediction methods are designed to make final prediction and view consistency is additionally considered to make selection criteria. Finally, we demonstrate the effectiveness of the proposed methods through experiments on real-world datasets.