Title of article :
A meta-heuristicapproachforimprovingtheaccuracyinsome
classification algorithms
Author/Authors :
Huy NguyenAnhPham، نويسنده , , EvangelosTriantaphyllou، نويسنده ,
Issue Information :
ماهنامه با شماره پیاپی سال 2011
Abstract :
Currentclassificationalgorithmsusuallydonottrytoachieveabalancebetweenfittingand
generalizationwhentheyinfermodelsfromtrainingdata.Furthermore,currentalgorithmsignore
the factthattheremaybedifferentpenaltycostsforthefalse-positive,false-negative,andunclassifiable
types. Thus,theirperformancemaynotbeoptimalormayevenbecoincidental.Thispaperproposesa
meta-heuristicapproach,calledtheConvexityBasedAlgorithm(CBA),toaddresstheseissues.Thenew
approachaimsatoptimallybalancingthedatafittingandgeneralizationbehaviorsofmodelswhen
sometraditionalclassificationapproachesareused.TheCBAfirstdefinesthetotalmisclassificationcost
(TC) asaweightedfunctionofthethreepenaltycostsandthecorrespondingerrorratesasmentioned
above. Nextitpartitionsthetrainingdataintoregions.Thisisdoneaccordingtosomeconvexity
propertiesderivablefromthetrainingdataandthetraditionalclassificationmethodtobeusedin
conjunctionwiththeCBA.NexttheCBAusesageneticapproachtodeterminetheoptimallevelsof
fitting andgeneralization.The TC is usedasthefitnessfunctioninthisgeneticapproach.Twelvereal-
life datasetsfromawidespectrumofdomainswereusedtobetterunderstandtheeffectivenessofthe
proposedapproach.ThecomputationalresultsindicatethattheCBAmaypotentiallyfillinacriticalgap
in theuseofcurrentorfutureclassificationalgorithms.
Keywords :
classification , Fitting , False positive , Generalization , False negative , Unclassifiable , Convex region , Genetic algorithms , Optimization
Journal title :
Computers and Operations Research
Journal title :
Computers and Operations Research
