利用置信度重取样的SemiBoost-CR分类模型

结合半监督学习和集成学习方法,提出了一种基于置信度重取样的SemiBoost-CR分类模型.给出了基于标注近邻与未标注近邻的置信度计算公式,按照置信度重采样,不仅选取一定比例置信度较高的未标注样本,而且选取一定比例置信度较低的未标注样本,分别以不同的策略加入到已标注的训练样本集,引入置信度高的未标注样本,用以提高基分类...

Advanced SemiBoost-CR Categorization Model Utilizing Confidence-Based Resampling

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.