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基于协同进化云的属性集成多代理约简算法（英文）.docx

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基于协同进化云的属性集成多代理约简算法（英文） Title:AMulti-AgentAttributeReductionAlgorithmbasedonCollaborativeEvolutionaryCloud Abstract: Attributereductionisanimportanttaskinthefieldofdataminingandmachinelearning,whichaimstoremoveredundantandirrelevantattributeswhilepreservingasmuchinformationaspossible.Traditionalattributereductionalgorithmsoftensufferfromhighcomputationalcomplexityandlimitedscalability.Inthispaper,weproposeanovelmulti-agentattributereductionalgorithmbasedoncollaborativeevolutionarycloud.Thealgorithmleveragesthebenefitsofcloudcomputingandthecollectiveintelligenceofmultipleagentstoimprovetheefficiencyandeffectivenessofattributereduction.Experimentalresultsonbenchmarkdatasetsdemonstratethesuperiorityoftheproposedalgorithmcomparedtoexistingapproachesintermsofcomputationtimeandthequalityofthereducedattributes. 1.Introduction: Attributereductionplaysacrucialroleinmachinelearninganddataminingtasksasithelpsreducethedimensionalityofdatasetswhilemaintainingthesalientfeatures.Traditionalattributereductionalgorithmsoftenfacechallengesintermsofcomputationalcomplexityandscalability,especiallyforlarge-scaledatasets.Toaddresstheseissues,weproposeamulti-agentattributereductionalgorithmbasedoncollaborativeevolutionarycloud.Theproposedalgorithmharnessesthepowerofcloudcomputingtodistributethecomputationamongmultipleagents,therebyimprovingefficiencyandscalability. 2.RelatedWork: Inthissection,wereviewexistingattributereductionalgorithms,includingroughset-basedapproaches,geneticalgorithms,andinformationtheory-basedmethods.Weanalyzetheirstrengthsandlimitationsandhighlighttheneedforamoreefficientandscalableapproach. 3.CollaborativeEvolutionaryCloud: Weintroducetheconceptofcollaborativeevolutionarycloud,whichcombinestheadvantagesofcloudcomputingandevolutionaryalgorithms.Thecollaborativeevolutionarycloudallowsmultipleagentstoworkcollaborativelyusingdistributedcomputingresources,enablingfasterandmoreefficientattributereduction. 4.Multi-AgentAttributeReductionAlgorithm: Wepresentthedetailedstep