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dc.contributor.author邓少军
dc.contributor.author冯少荣
dc.contributor.author林子雨
dc.date.accessioned2018-11-26T08:55:21Z
dc.date.available2018-11-26T08:55:21Z
dc.date.issued2017
dc.identifier.citation厦门大学学报. 自然科学版,2017,(2):231-236
dc.identifier.issn0438-0479
dc.identifier.other10.6043/j.issn.0438-0479.201604043
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/165715
dc.description.abstract为了提高代价敏感分类算法MetaCost的准确率,降低错分代价,提出了多类别问题下的一种代价敏感分类算法(简称D-MetaCost算法).该算法; 利用MetaCost算法,通过多次取样生成多个模型,依据它们的分类准确率,选择其中准确率较高的前几个基分类器,将它们与最后阶段新生成的分类器聚集; 在一起得到最终分类模型.实验表明,D-MetaCost 算法在准确率和代价方面比经典的MetaCost算法有明显的改进和提高.
dc.description.abstractCost-sensitive classification is an important research topic in the; classification problem. In order to improve the accuracy of; MetaCost,which serves as a cost-sensitive classification algorithm, and; reduce its misclassification cost,we propose a new cost-sensitive; algorithm,called D-MetaCost,for multi-class problems.In D-MetaCost; algorithm,we can calculate the accuracy of multiple models generated in; the beginning of MetaCost algorithm,and select first few base; classifiers with higher accuracy,then integrate them together with the; new model of the last stage to obtainthe final classification model.; Experimental results show that the proposed algorithm enjoys obvious; improvements in accuracy and cost in comparison with the classical; MetaCost algorithm.
dc.description.sponsorship国家自然科学基金; 国家社会科学基金重大项目
dc.language.isozh_CN
dc.subject分类代价
dc.subject代价敏感
dc.subject集成学习
dc.subjectMetaCost
dc.subjectD-MetaCost
dc.subjectclassification cost
dc.subjectcost-sensitive
dc.subjectensemble
dc.subjectlearning
dc.subjectMetaCost
dc.subjectD-MetaCost
dc.title一种新的多分类代价敏感算法
dc.title.alternativeA New Multi-class Cost-sensitive Algorithm
dc.typeArticle


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