计算机应用研究2013，Vol.30Issue(4)：1085-1089,5.DOI:10.3969/j.issn.1001-3695.2013.04.034
半监督学习在不平衡样本集分类中的应用研究
Semi-supervised learning in imbalanced sample set classification
摘要
Abstract
Higher error rate emerged in the minority class of samples when make classification on imbalanced sample set, but most algorithms in semi-supervised learning are based on normal data set. This paper studied the effectiveness of a semi-supervised collaboration classification method. Because of the further enhanced classifier difference, this algorithm had good performance on classification of imbalanced sample set. It established classification model based on the above algorithm, and used this model to make classification with bridge structural health monitoring data, the compared results of which demonstrated the applicability to imbalanced sample set. Therefore it validated the effectiveness of the algorithm.关键词
不平衡样本集/半监督协同分类方法/分类器差异性/分类模型/桥梁结构健康数据Key words
imbalanced sample set/ semi-supervised collaboration classification method/ classifier difference/ classification model/ bridge structural health data分类
信息技术与安全科学引用本文复制引用
于重重,商利利,谭励,涂序彦,杨扬..半监督学习在不平衡样本集分类中的应用研究[J].计算机应用研究,2013,30(4):1085-1089,5.基金项目
国家自然科学基金资助项目(61070182) （61070182）
北京市组织部优秀人才资助项目(2010D005003000008) （2010D005003000008）
北京市学科建设项目(PXM2012_014213_0000_74,PXM2012_014213_0000_23) （PXM2012_014213_0000_74,PXM2012_014213_0000_23）
