Semi-supervised patent text classification method based on improved Tri-training algorithm

An improved information gain (IG) algorithm was proposed, in order to solve the problem that the IG algorithm can only be used to investigate the contribution of features to the whole system, but not for a single category. The weight coefficient is introduced to adjust the information gain values of features important for classification, so the inhomogeneity of distribution of a word among categories can be better considered. A semi-supervised classification method based on the improved Tri-training algorithm was proposed, aiming at the bottleneck problem of training set labeling in traditional patent automatic classification. The prediction probability thresholds of the same unlabeled sample's category of three classifiers are dynamically changed by tracking the distribution of sample categories of training sets after each iteration. As a result, the influence of noise data is reduced and the full advantage of the unmarked training samples is achieved. Results indicate that the proposed classification method has positive automatic classification effect in the case of fewer labeled training samples, and the generalization ability of the classifier can be improved through appropriately increasing unlabeled sample data. © 2020, Zhejiang University Press. All right reserved.