Subsampling oriented active learning method for multi-category classification problem

Because the computational amount of the traditional active learning method increases exponentially with the increase of problem size, it is difficult to apply to the large-scale multi-category data classification tasks. To solve this problem, a subsampling-based active learning (SBAL) algorithm is designed. This algorithm integrates unsupervised clustering algorithm with traditional active learning method, and adds subsampling operation between them. This operation can significantly reduce the time complexity of the algorithm, reduce the experimental time-consuming on the basis of ensuring the accuracy of the experiment, so as to deal with the classification problem of large-scale data sets more efficiently. The experimental results show that the experimental performance of the SBAL algorithm is better than that of the traditional active learning algorithm, which proves that the proposed method can break through the limitation that the traditional active learning method can not deal with multi-category classification of large-scale data sets. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.