Abstract: Semi-supervised ensemble learning(SSEL) is a combinatorial paradigm by fusing semi-supervised learning and ensemble learning together,which improves the diversity of ensemble learning by introducing unlabeled samples and at the same time solves the problem of insufficient sample size for ensemble learning.In addition,SSEL can improve the generalization capability of classification system by integrating multiple classifiers trained on the highly-credible labeled samples.The existing researches have proved the mutual benefit between semi-supervised learning and integrated learning from both theoretical and practical perspectives.The existing SSEL algorithms are unable to make full use of the unlabeled samples,which limit their prediction capabi-lities when handling the classification problems with less labeled samples.This paper proposes a novel classification uncertainty minimization-based semi-supervised ensemble learning(CUM-SSEL) algorithm,which introduces the information entropy as the criterion of confidence and uses the characteristics of information entropy to minimize the classification uncertainty in the process of predicting unlabeled samples.The feasibility,rationality and effectiveness of CUM-SSEL algorithm are verified based on a series of persuasive experiments.Experimental results demonstrate that CUM-SSEL is a valid algorithm to deal with the semi-supervised learning problems.

Key words: Semi-supervised ensemble learning, Ensemble learning, Semi-supervised learning, Classification uncertainty, Confidence, Information entropy