搜索
356Vol.35,No.620096ACTAAUTOMATICASINICAJune,200911111,,.,,,.:,,.,,,,TP273ReviewonData-basedDecisionMakingMethodologiesWANGHong-Wei1QIChao1WEIYong-Chang1LIBin1ZHUSong1AbstractTherearetwodistinguishingcharacteristicsformoderndecisionmakingproblemsincomparisonwiththetraditionalsituation:oneistheavailabilityoflargeamountoforiginaldataemergingwiththedevelopmentofsystemautomationtechnology;theotheristhecomplexityanduncertaintyunderlyingthereal-lifedecisionproblems,whichmakeitinfeasibletoestablishprecisemodels.Traditionalmodel-baseddecisionmakingmethodologiesareine±cientunderthiscircumstance.Therefore,anumberofresearchworkshavebeenconductedondata-baseddecisionmakingmethodologies.Thispaperreviewstheprevalentdata-baseddecisionmakingmethodologiesfromthreeaspectsbasedonthecharacteristicsoftheconsidereddecisionproblems:classi¯cationmethodology,decisionanalysismethodology,andoptimizationmethodology.Thecharacteristics,developmenthistory,andperspectivearesummarizedforeachspeci¯cmethodology.KeywordsData-based,decisionmaking,classi¯cation,decisionanalysis,optimization1978Simon\,\.,,20,.,.19031930,TaylorGilbrechGantt,.,,2060.70,,,Acko®19732009-02-132009-03-20ReceivedFebruary13,2009;inrevisedformMarch20,2009(60674085)SupportedbyNationalNaturalScienceFoundationofChina(60674085)14300741DepartmentofControlScienceandEngineering,HuazhongUniversityofScienceandTechnology,Wuhan430074DOI:10.3724/SP.J.1004.2009.00820.,,,,,,,.,,,.,.,.,:1):.,.,6:821,..2):,(),,.().3):.,,..,,,..1,.,,,.,,.,,,,,,,.,.,,,.1.1,.,.,,NP.ID3[1],,.,C4.5[2]EC4.5[3]..[4¡6].IBM[7].,,,,[8¡9].,.,.,.,[10][11¡13][14¡15].,:1);2);3);4),;5).1.2(Supportvectormachine,SVM).Vap-nik60[16].1971SVMVC[17].,[18],[19],SVM[20],SVM[21]..,,,,.SVM,,,SVM[22][23¡27][28¡32][33¡35][36¡39].,:1);2),82235;3),.1.3(),(),,,,,,,\.,.1984Morlet..Grossmann,,.1988,Daubecies.,..,CT,.,[40][41](Haar)[42].1.4.,,,.,.:,,.Tryon1939,.SneathSokal1973,,.K-[43],,,,.K-,,\,.2001[44].K-K-,SPSSSAS..,,,,().,,:[45][46][47][48].1.5,,,.,:,,;,[49],;,,.(Multi-layerperceptron,MLP)[50],,MLP.MLP,:[51][52].(Radialbasisfunction,RBF),:,.:Koho-nen[53],(Adaptiveresonancetheory,ART)[54],Hop¯eld[55].,[56][57][58][59].,,,.1.6,,Zadeh19656:823,,,,.[60¡61].,..[62],[63¡65],[66¡67],[68¡71],[72¡75].,[76¡77][78][79].1.71982Pawlak[80],,[81].,,,,[82].:[83][84][85].,:.,.,,,,,,;,,,;,,.,:1),,;2),,,;3),,,.,.2,,.,,,.2.1,,1763\AnEssayTowardsSolvingaProblemintheDoctrineofChances,.2050,Je®ryWaldSavageRai®aSehlaiferrLindlyDeFinetti,.,,.,,,,,,.,[86¡87],..1986Pearl,1988,.1989AndreassenMUNIN.Shafer1990.,,.:90,,;90,,82435;20,,.,..[88][89¡90][91][92][93][94][95]DNA[96¡97][98][99][100].2.21,.,,,.,.,,.1Fig.1Frameworkofrule-basedreasoningmodels2.2.1,,.DempsterShafer,Dempster-Shafer(DS).,DS,(Evidentialreasoning,ER).ERDS[101¡104].ER:[101][105][106¡107][108¡109][110][111],[112].,[113¡114]ERRIMER,IF-THEN,.RIMER[115¡116][117].:[113¡114][116],,,,[118],MarkovianDirichlet[119].2.2.2Zadeh,2070,.,.[120¡121],[122¡123]..:/,.{{[124]..80,[125],,.[126].:[127¡128],[129],[130],[131],[132],[133],[134],[135],[136],[137¡138],[139].2.3:,,().Charnes19786:825(Dataenvelopmentanalysis,DEA),,CCR,\\\.,BCC[140]CCGSS[141],\.[142]DEA,[143]DEA,[144](Rangeadjustedmea-sure,RAM),[145]DEA,[146]DEA,[147](Slacks-basedmeasure,SBM),.,DEA,[144],[148][149][150].2.4(Timeseriesanalysis)2070.,,.,.,.,,,,:,,;,,.,,,.,,:[151],[152],[153],[154].2.5,.,[155¡157],.,,,,,.[158].320,.,,.2050,DantzigCharnesCooper,.,,[159¡160].,,.,,,.,,,,().,,,,\\,,,.,,,,...3.1,.,.:,;,.,,.2070,82635Soyster,.,Falk..2090,Ben-TalNemirovski[161¡163]El-GhaouiLebret[164¡165],.,,,[166¡169].,[170],0-1[171],,,[172].:[173],[174],[175],[176].3.2.,,,.,.BellmanZadeh1970.,:.,..,[177¡179][180].,[180],,[181¡182][183][184].3.3(Neuro-dynamicpro-gramming),,\\,,,.,.,.BertsekasTsitsiklis1995IEEE,,,[185¡187].,.,,,,\\.,,,.TD[188]Q[189]R[190]Q-P[191]SMART[192]Relaxed-SMART[193].,:[194][195¡196][197][198][199¡200][201].3.4,:1)[202¡203];2),;3),;4);5).,,,.,Hop¯eldBP[204¡208].4,.,,,,,.,,:1),,,6:827,;2),,,,;3),,,.References1QuinlanJR.Inductionofdecisiontrees.MachineLearning,1986,1(1):81¡1062RuggieriS.E±cientC4.5.IEEETransactionsonKnowledgeandDataEngineering,2002,14(2):438¡4443BreimanL,FriedmanJ,StoneCJ,OlshenRA.Classi¯ca-tionandRegressionTrees.NewYork:ChapmanandHall,19844CatlettJ.MegaInduction:MachineLearningonVaryLargeDatabases[Ph.D.dissertation],UniversityofSydney,Aus-tralia,19915ChanPK,StolfoSJ.Ontheaccuracyofmeta-learningforscalabledatamining.JournalofIntelligentInformationSystems,1997,8(1):5¡286GehrkeJ,RamakrishnanR,GantiV.RainForest-aframe-workforfastdecisiontreeconstructionoflargedatasets.DataMiningandKnowledgeDiscovery,2000,4(2-3):127¡1627MehtaM,AgrawalR,RissanenJ.SLIQ:afastscalableclas-si¯erfordatamining.In:Proceedingsofthe5thInterna-tionalConferenceonExtendingDatabaseTechnology.Avi-gnon,France:Springer,1996.18¡328Utgo®PE.Incrementalinductionofdecisiontrees.MachineLearning,1989,4(2):161¡1869CrawfordSL.ExtensionstotheCARTalgorithm.In-ternationalJournalofMan-MachineStudies,1989,31(2):197¡21710ArgentieroP,ChinR,BeaudetP.Anautomatedapproachtothedesignofdec