An adaptive active learning algorithm with informativeness and representativeness

Abstract

Active learning focuses on selecting a small subset of the most valuable instances for labeling to learn a highly accurate model. Considering informativeness and representativeness of unlabeled instances is significant for a query, some works have been done about combing informativeness and representativeness criteria. However, most of them are generally in a fixed manner to balance these criteria, and difficult to find suitable sampling strategies and weights of informativeness and representativeness for various datasets. In this paper, an adaptive active learning method ALIR is proposed to address these limitations. Firstly, an adaptive active learning framework is represented, in which the weight of informativeness and representativeness criteria can be dynamically updated by the feedback of previous learning processes. Secondly, by formulating the active learning as a Markov decision process, ALIR can adaptively select the suitable sampling strategies according to the reward of the learning process. Finally, extensive experimental results over several benchmark datasets and two real classification datasets demonstrate that ALIR outperforms several state-of-the-art methods. Different from traditional active learning algorithms, ALIR can adaptively select sampling strategies and adjust the weights simultaneously, which helps it more feasible in the application.