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两阶段半监督加权朴素信念分类模型

A Two-Stage Semi-Supervised Weighted Naive Credal Classification Model
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摘要 针对目前半监督分类算法中未考虑缺失属性隐含信息和算法复杂度高的情况,改进了朴素信念分类,提出了两阶段半监督加权朴素信念分类模型。与直推支持向量机对比实验结果表明两阶段半监督加权朴素信念分类模型减少了分类时间,并且在其能够明确分类的样本上的正确率与直推支持向量机相当,是一种有效的不完整数据分类算法。 Aiming at full use of implicit information in the incomplete data sets and reducing the high computational complexity of semi-supervised classification algorithms,this paper improves nave credal classifier,and proposes a two-stage semi-supervised weighted naive credal classification model,in which the process of semi-supervised classification is divided into two stages.Simulation results of comparative experiment with TSVM verify that this classification model is efficient.
作者 祁瑞华 杨德礼 李慧芬
机构地区 大连外国语学院现代教育技术中心 大连理工大学管理学院系统工程研究所
出处 《运筹与管理》 CSCD 北大核心 2011年第5期156-161,共6页 Operations Research and Management Science
基金 国家自然科学基金重大项目(708890080) 教育部人文社科青年项目(11YJCZH131) 辽宁经济社会发展立项课题(2011LSIKTJJX-75)
关键词 模式识别 分类 半监督 加权 两阶段 pattern recognition classification semi-supervised weighing two-stage
分类号 TP274.2 [自动化与计算机技术—检测技术与自动化装置]
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参考文献10

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二级参考文献17

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运筹与管理
2011年 第5期

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