一种新的不平衡数据v - NSVDD多分类算法*

南京大学学报(自然科学版) ›› 2013, Vol. 49 ›› Issue (2): 150–158.

一种新的不平衡数据v - NSVDD多分类算法*

刘小平¹，徐桂云¹，任世锦²**，杨茂云^1·2

出版日期:2015-11-03 发布日期:2015-11-03
作者简介: (1.中国矿业大学机电工程学院，徐州，221116;2.江苏师范大学计算机学院，徐州，221116)
基金资助:
国家自然科学基金(60974056)

A new unbalanced data v-NSVDD multiclass algorithm

Liu Xiao一Ping¹，Xu Gui-Yun¹ ,Ren Shi一Jin² ,Yanh Mao-Yun^1.2

Online:2015-11-03 Published:2015-11-03
About author: (1. School of Mechanical and Electrical Engineering,China University of Minim and Technology,Xuzhou，
221116,China;2. School of Computer Science and Technology,Jiangsu Normal Univcrsity,Xuzhou，221116 , China)

摘要/Abstract

摘要： 分析了多类支持向量数据描述(support vector data description,SVDD)算法存在的问题，提出
一种新的不平衡数据二一NSVDD多分类算法.该方法借鉴了二SVM方法以及带有负类的SVDD的思
想，并基于不同类别样木间隔最大原理，较好地克服噪声和在野点的影响，提高了分类模型的泛化性能;
通过样木加权的方法解决了不平衡类别样木预测精度低的问题，并在理论上给出了根据类别样木数量
设置样木加权系数的方法.针对实际应用存在大量复杂、非线性分类数据，通过核方法把上述线性分类
算法推]’一到非线性数据分类情形.由于现有的多分类器无法实现拒判，而且每个分类器的核函数参数不
同，导致数据点与各个超球中心距离的计算结果与实际距离不相符，影响了数据判决结果的准确性和可
靠性.针对上述问题，给出基于相对距离和K-NN规则相结合的多分类方法，提高了分类结果的准确性
和可靠性.使用Benchmark数据集进行仿真实验，结果表明木算法能够获得较低的分类误差，能够有效
处理样木不平衡问题.

Abstract: Based on analysis of the problems of state of the art support vector data description(SVDD)methods for multi-
class classification,a new unbalance data v-NSVDD algorithm for multiclass classification is proposed. lnpired from the idea
of rrSVM,SVDD with negative samples(NSVDD)and classification margin maximum principle,the method can not only ef-
fectivcly reduce the perturbances of noises and outlicrs,but also correct the problem existed in optimizztion problem in Ref
[8],which considerably improves the generalization performance of the proposed algrithm. The training samples are weigh-
ted to deal with the unbalanced data classification problem,and also the weight coefficients can be conveniently calculated in
theory according to the number of each class samples. Zaking into account of complexity and nonlincarity of the classfication
samples in many practical applications,we extend the proposed linear algorithm to nonlinear cases by means of kernel trick.
Since many nonlinear unbalanced data v-NSVDD models are needed for multiclass data and each model is seperatcly devel-
oped,the corresponding kernel parameters are very different. As a result,distences between a fixed sample and hypersphere
centers in different reproduction kernel hilbert spaces(RKHSs)are not inconsistent with pratical distances. Furtherly,most
existing multiclass classification methods are lack of rejection decision, which can impair the classification performance and
reliability of decision. Relative distance measure is first proposed to achieve the consistent distances in RKHSs,and then a
multiclass classification method is developed by combining relative distance with K-NN calssfication rule to deal with the a-
bove problems.The benchmark testing results show that the proposed method can provide lower classification errors and deal
with unbalanced data problem.

刘小平¹，徐桂云¹，任世锦²**，杨茂云^1·2. 一种新的不平衡数据v - NSVDD多分类算法*[J]. 南京大学学报(自然科学版), 2013, 49(2): 150–158.

Liu Xiao一Ping¹，Xu Gui-Yun¹ ,Ren Shi一Jin² ,Yanh Mao-Yun^1.2. A new unbalanced data v-NSVDD multiclass algorithm[J]. Journal of Nanjing University(Natural Sciences), 2013, 49(2): 150–158.

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