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一种新颖的离散化算法及其应用 Title:NovelDiscretizationAlgorithmanditsApplications Abstract: Inthispaper,weintroduceanoveldiscretizationalgorithmandexploreitspotentialapplications.Discretizationistheprocessoftransformingcontinuousvariablesintodiscretecategoriesorintervals,whichisessentialinmanymachinelearninganddataanalysistasks.Theproposedalgorithmaimstoovercomethelimitationsandchallengesofexistingdiscretizationmethods,providingamoreaccurateandefficientapproach. 1.Introduction Discretizationiswidelyusedinvariousfieldssuchasdatamining,classification,anddecisionsupportsystems.Traditionaldiscretizationmethodssufferfromdrawbackslikeinformationloss,sensitivitytodatadistribution,andsuboptimalbinning.Thenovelalgorithmpresentedinthispaperaddressestheseissuesbyprovidingamoreflexibleandadaptiveapproach. 2.Methodology Theproposeddiscretizationalgorithmleveragesacombinationofstatisticalandmachinelearningtechniques.Itoptimizesthefollowingkeyaspects: 2.1AdaptiveBinning Unlikefixed-widthbinningintraditionalmethods,thealgorithmdynamicallyadjuststhebinwidthbasedondatadistribution.Thisadaptabilityensuresthateachbincapturesadequateinformationwhilemaintainingbalanceamongcategories. 2.2InformationGainRatio InformationGainRatioisutilizedtoevaluatetheimportanceofeachfeatureandguidethebinningprocess.Ithelpstoidentifythresholdswheresignificantchangesoccurinthetargetvariable,resultinginmoreinformativeanddistinctcategories. 2.3UnsupervisedClustering Toenhancethealgorithm'sperformance,itincorporatesunsupervisedclusteringalgorithmstodiscoverinherentpatternsandstructuresinthedata.Byclusteringsimilardatapointstogether,morerefinedandmeaningfulcategoriesareobtained. 3.ExperimentalResults Toevaluatetheeffectivenessoftheproposedalgorithm,weconductedexperimentsonvariousdatasetsfromdifferentdomains.Acomparativeanalysiswasperformedagainsttraditionaldiscretizationmethods,includingtheEqualWidthandEqualFrequencyapproaches. 3.1InformationPreservation Ouralgorithmdemonstratedsuperiorinformationpreservationcomparedtotradit