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基于信息熵的支持向量数据描述分类 被引量：3

Classification method for SVDD based on information entropy
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摘要 针对现有的支持向量数据描述(SVDD)在解决分类问题时通常存在盲目性和有偏性,在研究信息熵和SVDD分类理论的基础上,提出了改进两类分类问题的E-SVDD算法。首先对两类样本数据分别求出其熵值;然后根据熵值大小决定将哪类放在球内;最后结合两类样本容量以及各自的熵值所提供的分布信息,对SVDD算法中的C值重新进行定义。采用该算法对人工样本集和UCI数据集进行实验,实验结果验证了算法的可行性和有效性。 Most of Support Vector Data Description（SVDD） methods have blindness and bias issues when working on two-class problems.The authors proposed a new SVDD method based on information entropy.In this algorithm,firstly,the entropy values were resolved respectively of the two classes of samples.Secondly,according to the size of the value,one class was placed inside the ball.Finally,the penalty was given based on the information provided by the sizes of the two sample data and their entropy values.The efficiency of this algorithm was verified by using artificial data and UCI datasets for the data imbalanced classification problem.The experimental results on artificial data sets and UCI data sets show the feasibility and effectiveness of the proposed method.
作者 何伟成 方景龙
机构地区 杭州电子科技大学计算机学院
出处 《计算机应用》 CSCD 北大核心 2011年第4期1114-1116,共3页 journal of Computer Applications
基金 国家自然科学基金资助项目(60874074) 浙江省科技计划重点项目(2009C14032)
关键词 信息熵 分布特性 支持向量数据描述 分类 information entropy distribution character Support Vector Data Description（SVDD） classification
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献8

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二级参考文献10

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共引文献13

同被引文献36

  • 1汪景宁.安徽统计年鉴[M].北京:中国统计出版社,2008.
  • 2陈斌,冯爱民,陈松灿,李斌.基于单簇聚类的数据描述[J].计算机学报,2007,30(8):1325-1332. 被引量：18
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  • 8TAX D M J,DUIN R P W.Support vector data description[J].Machine Learning,2004,54(1):45-66.
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引证文献3

二级引证文献2

计算机应用
2011年 第4期

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