不完备信息系统的完备化及其上的知识获取的开题报告IntroductionIncompleteinformationsystemshavebeenusedwidelyinvariousfieldstoidentifypatterns,makepredictions,andsupportdecision-makingprocesses.However,incompleteinformationsystemssufferfromuncertaintiesduetomissingorincompletedata.Hence,dataqualityisacriticalissueforsuchsystems.Inthisreport,weproposeamethodtoimprovethecompletenessofanincompleteinformationsystemandenhanceknowledgeacquisitiononsuchasystem.BackgroundIncompleteinformationsystemsrefertosystemswhereoneormoreattributesofadatasetaremissingorincomplete.Itisgenerallyassumedthatacompletedatasetwouldyieldbetterresultsindataanalysistaskssuchasdataminingandmachinelearningalgorithms.However,inmanypracticalproblems,collectingacomprehensivedatasetcanbechallenging,expensive,ortime-consuming.Forexample,medicaldataisoftenincompleteduetothereluctanceofsomepatientstodisclosepersonalinformationordiseasehistory.MethodologyToenhancedataqualityandknowledgeacquisitiononincompleteinformationsystems,weproposetousedataimputationtechniques.Dataimputationaimstofillinthemissingvaluesofadatasetbyinferringfromavailabledata.Thereexistvariousdataimputationmethods,includingmeanimputation,regressionimputation,andmultipleimputation.Wewillfocusonmultipleimputationasitisamorerobustandadvancedmethod.Multipleimputationconsistsofcreatingmultipleimputeddatasetsbyfillinginthemissingvaluesmultipletimesusingdifferentimputationmodels.Thesemultipleimputeddatasetsarethenmerged,andtheanalyticaltaskisperformedonthecombineddataset.Theresultisamorepreciseestimatethattakesintoaccounttheuncertaintyintroducedbythemissingdata.Toapplymultipleimputation,wewillusetheRstatisticalsoftware.Rprovidesseveralpackagesformultipleimputation,includingmiceandAmelia.Wewillcomparethesepackagesandselecttheonethatyieldsthebestresultsonourdataset.Wewillalsoevaluatetheperformanceofourmethodonseveraldatasetsandcompareitwiththatofcompletedatasets.ExpectedResultsWeexpectthatourapproachwillenhancethecompletenessofanincompleteinformationsystem,thusi