Multi-label text classification algorithm based on semi-supervised learning

Abstract

The standard Co-Training algorithm can effectively improve the performance of the classifier by using unlabeled data. In fact, however, it is difficult to meet the requirement of sufficient and redundant views At the same time, the constraints of Co-Training is further relaxed to solve the problem of inaccurate confidence estimation of unlabeled samples. In this paper, imml-knn and mlplsa-knn are used to train the basic classifiers with great difference. By using unlabeled data in the way of providing Pseudotag sample to each other, a Multi-Label Text Classification Algorithm CT-MLTC based on Co-Training is proposed. Experimental results show that the algorithm has good performance in multi label text classification tasks with more unlabeled data.