电子学报2013，Vol.41Issue(1)：35-41,7.DOI:10.3969/j.issn.0372-2112.2013.01.007
一种增强差异性的半监督协同分类算法
A Semi-supervised Collaboration Classification Algorithm with Enhanced Difference
摘要
Abstract
Tri-Training algorithm in semi-supervised learning broke the restriction of previous algorithms on sufficient and redundant views and raised label efficiency by applying three classifiers to deal with labeled confidence.In order to further improve classifiers' performance through enhancing the difference between them, a semi-supervised collaborative classification algorithm with enhanced difference that applies three different classifiers was presented in this paper.Taking the might-be performance deterioration led by random sampling during the updating of classifying models into consideration, a method of stratified sampling based on labeled class was used by the algorithm to sample from the labeled sample sets, and the method of weighted voting based on classification accuracy realized the classifier ensemble, as a result the prediction accuracy is raised.Performance comparison between the proposed algorithm and Tri-Training algorithm was made through experiments,and the results show effectiveness of the former.关键词
半监督协同分类算法/Tri-Training算法/增强差异性策略/分层抽样法Key words
semi-supervised collaboration classification algorithm/Tri-Training algorithm/strategy of enhancing difference/stratified sampling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,王竞燕..一种增强差异性的半监督协同分类算法[J].电子学报,2013,41(1):35-41,7.基金项目
国家自然科学基金(No.61070182) （No.61070182）
北京市组织部优秀人才资助项目(No.2010D005003000008) （No.2010D005003000008）
北京市学科建设项目(No.PXM2012_014213_0000_74) （No.PXM2012_014213_0000_74）
北京市学科建设项目(No.pxm_2012_014213_000023) （No.pxm_2012_014213_000023）
