A Safe Semi-supervised Classification Algorithm Using Multiple Classifiers Ensemble

Abstract

In order to improve the performance of semi-supervised learning, a safe semi-supervised classification algorithm using multiple classifiers ensemble (S3C-MC) is proposed. First, unlabeled samples are filtered and unlabeled samples with small ambiguity are selected for semi-supervised learning. Next, the labeled training set is sampled to multiple subsets and they generate multiple classifiers to predict the filtered unlabeled sample respectively. The predicted label is formed by multiple classifiers with weighted voting mechanism, and the weight of classifier is changing constantly according to the correctness of the prediction for unlabeled samples by classifier. Then, security verification is carried out to ensure that the classifier evolves in the direction of error reduction when the new sample is added. Only the label making classifiers error lower and having the same predictive value with the three classifiers in security verification is added into the labeled set to expand the number of labeled sets. Finally, the algorithm iterates until the unlabeled sample set is empty. The experiment is carried out on the UCI data set and the result shows that the proposed S3C-MC has good safety and has a higher classification rate.