In active learning, the learner is required to measure the importance of unlabeled samples in a large dataset and select the best one iteratively. This sample selection process could be treated as a decision making problem, which evaluates, ranks, and makes choices from a finite set of alternatives. In many decision making problems, it usually applied multiple criteria since the performance is better than using a single criterion. Motivated by these facts, an active learning model based on multi-criteria decision making (MCMD) is proposed in this paper. After the investigation between any two unlabeled samples, a preference preorder is determined for each criterion. The dominated index and the dominating index are then defined and calculated to evaluate the informativeness of unlabeled samples, which provide an effective metric measure for sample selection. On the other hand, under multiple-instance learning (MIL) environment, the instances/samples are grouped into bags, a bag is negative only if all of its instances are negative, and is positive otherwise. Multiple-instance active learning (MIAL) aims to select and label the most informative bags from numerous unlabeled ones, and learn a MIL classifier for accurately predicting unseen bags by requesting as few labels as possible. It adopts a MIL algorithm as the base classifier, and follows an active learning procedure. In order to achieve a balance between learning efficiency and generalization capability, the proposed active learning model is restricted to a specific algorithm under MIL environment. Experimental results demonstrate the effectiveness of the proposed method. © 2014 Elsevier Ltd.
Bibliographical noteThis work was supported by the National Natural Science Foundation of China under the grant 61272289.
- Active learning
- Multi-criteria decision making
- Multiple-instance learning
- Support vector machine