Ambiguity-Based Multiclass Active Learning

Most existing works on active learning (AL) focus on binary classification problems, which limit their applications in various real-world scenarios. One solution to multiclass AL (MAL) is evaluating the informativeness of unlabeled samples by an uncertainty model and selecting the most uncertain one for query. In this paper, an ambiguity-based strategy is proposed to tackle this problem by applying a possibility approach. First, the possibilistic memberships of unlabeled samples in the multiple classes are calculated from the one-against-all-based support vector machine model. Then, by employing fuzzy logic operators, these memberships are aggregated into a new concept named $k$ -order ambiguity, which estimates the risk of labeling a sample among $k$ classes. Afterward, the $k$ -order ambiguities are used to form an overall ambiguity measure to evaluate the uncertainty of the unlabeled samples. Finally, the sample with the maximum ambiguity is selected for query, and a new MAL strategy is developed. Experiments demonstrate the feasibility and effectiveness of the proposed method.