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中南大学学报(自然科学版)

Journal of Central South University

第46卷    第6期    总第250期    2015年6月

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文章编号:1672-7207(2015)06-2074-07
鲁棒的加权孪生支持向量机
花小朋1, 2,丁世飞2, 3

(1. 盐城工学院 信息工程学院,江苏 盐城,224051;
2. 中国矿业大学 计算机科学与技术学院,江苏 徐州,221116;
3. 中国科学院 计算技术研究所 智能信息处理重点实验室,北京,100190
)

摘 要: 基于局部信息的加权孪生支持向量机(WLTSVM)借用类内及类间近邻图分别表示类内样本的紧凑性和类间样本的分散性,克服孪生支持向量机(TWSVM)欠考虑训练样本间相似性的缺陷,并且在一定程度上降低二次规划求解的计算复杂度。然而,WLTSVM仍不能充分刻画类内样本潜在的局部几何结构,并且存在对噪声点敏感的风险。基于以上不足,提出一种鲁棒的加权孪生支持向量机(RWTSVM)。与WLTSVM相比,RWTSVM的优势在于:选用热核函数定义类内近邻图权值矩阵,可以更好地刻画类内样本潜在的局部几何结构及蕴含的鉴别信息;用类间近邻图选取边界点,同时结合类内近邻图使得超平面远离边界点中权重较大的样本,降低算法对噪声点敏感的风险。人造数据集和真实数据集上的测试结果验证算法RWTSVM的有效性。

 

关键字: 孪生支持向量机;局部几何结构;噪声点;鲁棒性;分类

Robust weighted twin support vector machine
HUA Xiaopeng1, 2, DING Shifei2, 3

1. School of Information Engineering, Yancheng Institute of Technology, Yancheng 224051, China;
2. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;
3. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology,
Chinese Academy of Science, Beijing 100190, China

Abstract:Weighted twin support vector machine with local information (WLTSVM), as a variant of twin support vector machine (TWSVM), uses within-class graph and between-class graph to characterize the intra-class compactness and the inter-class separability, respectively. This makes WLTSVM improve the generalization capability of TWSVM by mining as much underlying similarity information within samples as possible and reduces the time complexity of TWSVM by reducing the support vectors for each class. Despite these advantages, WLTSVM can not fully reflect the local geometry manifold within samples because of using the within-class graph whose weight matrix is defined simply. Moreover, WLTSVM ignores the possible outliers because it chooses boundary points in the contrary class to construct the constraints. Thus a novel method, robust weighted twin support vector machine (RWTSVM) was proposed, which can better characterize the underlying local geometric structure and the descriminant information by using a hot kernel function to define the weight matrix of within-class graph, and reduce the influence of outliers by considering within-class weight of the boundary points used to construct constraints in WLTSVM. The experimental results on the artificial and real datasets indicate the effectiveness of RWTSVM method.

 

Key words: twin support vector machine; local geometric structure; outliers; robust; classification

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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