An active learning method based on three-way decision model

Active learning is one of the focuses in the field of machine learning, aiming to solve the unlabeled problem of samples. In this paper, a three-way decision model is applied to active learning. By introducing decision functions, the unlabeled samples are divided into three different parts: positive region, boundary region and negative region based on the uncertainty of unlabeled samples. Different solutions are adopted to process samples for each region. Then, an active learning method based on the three-way decision model, namely TWD_ctive, is developed. The most useful samples are selected using the active learning method, and are labeled by experts, so more effective models can be trained by the expanded training set. Compared with traditional passive learning, this method can choose the informational and representative samples to label, avoiding the redundant addition of sample. The models are continuously trained until the expected number of iterations or performance indicators are achieved. Experimental results show that the proposed algorithm has a better performance in measures F-value, AUC and the effectiveness of the algorithm is verified. © 2019, Editorial Office of Control and Decision. All right reserved.