关键词: boosting, 半监督分类, 朴素贝叶斯, 置信度, 重取样

Abstract: This paper proposes SemiBoost-CR, an enhanced categorization model which utilizing the confidence- based resampling technique and incorporating semi-supervised learning with ensemble learning. The confidence score is derived from the nearer labeled neighbors and unlabeled neighbors of the example. According to the
confidence-based resampling, not only the unlabeled examples with higher confidence score, but also the unlabeled ones with lower confidence score are selected and added to the labeled training set. The accuracy of the base classi-fier is to be improved by introducing the unlabeled data with higher confidence; the diversity among the base classi-fiers is further increased by introducing the unlabeled data with lower confidence. Experimental results show that SemiBoost-CR can boost the performance of Naive Bayesian text categorization.

Key words: boosting, semi-supervised categorization, Naive Bayesian, confidence, resampling