全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

?一种新的不平衡数据v-nsvdd多分类算法*

DOI: 10.13232/j.cnki.jnju.2013.01.003, PP. 150-158

Keywords: 支持向量数据描述(svdd),样木类别不平衡,多分类,拒判,超球软边界

Full-Text   Cite this paper   Add to My Lib

Abstract:

?分析了多类支持向量数据描述(supportvectordatadescription,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.

References

[1]  huanggx,chenhf,zhouzl,etal.two-classsupportvectordatadescription.patternrecognition,2011,44:320一329.
[2]  zhuml,liuxd,chensf.solvingtheproblemofmulticlasspatternrecognitionwithsphere-
[3]  structuredsupportvectormachines.journalofnanjinguniversity(naturalsciences),2003,39
[4]  (2);153-158.(朱美琳,刘向东,陈世福.用球结构的支持向量机解决多分类问题.南京大学学报(自然科学),2003,39(2);153-158).
[5]  maindescriptionfaultdetectionofmultimodalprocesses.expertsystemswithapplications,2012,39:2166一2171.
[6]  agery.ieeetransactionsongeoscienceandremotesensingletters,2011,8(2):384一388.
[7]  guosm,chenlc,tsaijsh.aboundarymethodforoutlierdetectionbasedonsupport
[8]  vectordomaindescription.patternrecognition,2009,42:77一83.
[9]  zhuxk,yangdg.amulticlasssupportvectordomaindescriptionforpatternrecognitionbased
[10]  onameasureofexpansibility.actaelectronicasinica,2009,37(3):464-469.(朱孝开,杨德贵.
[11]  基于推广能力测度的多类svdd模式识别方法.电子学报,2009,37(3):464-469).
[12]  mutt,nandiak.multiclassclassificationbasedonextendedsupportvectordatadescription,ieee
[13]  transactionsonsystem,man,andcybernetics一partb;cybernetics,2009,39(5):1206一1212.
[14]  daewonl,jaewookl.domaindescribedsupportvectorclassifierformulti-classificationproblems.
[15]  patternrecognition,2007,40;41一51.
[16]  taoq,wug,wangj.anewmaximummaeginalgorithmforone-classproblemsanditsboosting
[17]  implemention.patternrecognition,2005,38(10):1071一1077.
[18]  weixk,lofbergj,fengy,etal.enclosingma-chinelearningforclassdescription.lecturenotes
[19]  incomputerscience.springer-verlag,2007,4491:424一433.
[20]  dataanalysis,2007,52(1):309一324.
[21]  transactionsonneuralnetworks,2007,18(1):284~289.
[22]  zhaof,zhangjy,liuj.anoptimizationkernelalgorithmforimprovingtheperformanceofsup-
[23]  portvectordomaindescription.actaautomaticasinca,2008,34(9):1122-1127.(赵峰,张军
[24]  英,刘敬.一种改善支撑向量域描述性能的核优化算法.自动化学报,2008,34(9):1122-1127).
[25]  liaozm,hugy,zhaolw,etal.supportvecfordatadescriptionimplementedinclass-imbal-
[26]  ancelearning.journalofappliedscience-electronicsandinformationengineering,2008,26
[27]  (1):79-84.(缪志敏,胡谷雨,丁力等.svdd在类别不平衡学习中的应用.应用科学学报,2008,26(1):79一84).
[28]  zhengfh,xuh,lip,etal.miningknowlwdgefromunblanceddatabasedonv-supportvector
[29]  machine.journalofzhejianguniversity(fngi-neeringscience),2006,40(10):1682一1887.(郑
[30]  恩辉,许宏,李平等.基于v-svm的不平衡数据挖掘研究.浙江大学学报(工学版),2006,40(10):1682一1889).
[31]  issambk,clausw,mohamcdl.kernelk-meansclusteringbasedlocalsupportvectordo-
[32]  saklaw,chana,jij,etal.ansvdd-basedalgorithmfortargetdetectioninhyperspectralim
[33]  davidmjt,duinrpw.supportvectordatadescription.machinelearning,2004,54.45一66.
[34]  doliaan,harriscj,shawetj,etal.kernelellipsoidaltrimming.computationalstatisticand
[35]  fengam,xueh,liuxj,etal.enhancedoneclasssupportvectormachine.journalofcomput-eranddevelopment,2008,45(11):1858一1864.(冯爱民,薛晖,刘学军等.增强型单类支持向量机.计算机研究与发展,2008,45(11);1858-1864).
[36]  leeky,kimdw,leekh,etal.dcnsity-in-ducedsupportvectordatadescription,ieee
[37]  wangwl,wangzy,zhengjw,etal.dcnsity-in-duceddatadescriptionone-classclassficr.controlanddecision,2001,26(11);1665-1669.(王万良,王震宇,郑建炜等.密度诱导型数据描述单类分类机.控制与决策,2011,26(11);1665-1669).
[38]  michiedjs,taylorcc.machinelearning;neu-ralandstatisticalclassification,http;//www.
[39]  ncc.up.pt/iacc/mi./statlog/data.html,2011-03-12.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133