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文本分类中特征选择算法的分析与研究 Title:AnalysisandResearchonFeatureSelectionAlgorithmsinTextClassification Abstract: Inrecentyears,theexponentialgrowthindigitaltextdatahasledtoanincreasedinterestintextclassification,whichinvolvescategorizingtextdocumentsintopredefinedcategories.Featureselectionisacriticalstepintextclassification,asitaimstoidentifythemostrelevantanddiscriminativefeaturesfromalargesetoffeatures.Thispaperpresentsananalysisandresearchonvariousfeatureselectionalgorithmsinthecontextoftextclassification.Wediscusstheimportanceoffeatureselection,introducedifferenttypesoffeatureselectionalgorithms,evaluatetheirperformance,andanalyzetheirstrengthsandweaknesses.Throughthisanalysis,weaimtoprovideacomprehensiveunderstandingoffeatureselectionalgorithmsandtheirimpactontextclassificationaccuracyandefficiency. 1.Introduction Textclassificationhasbecomeincreasinglyimportantduetotherapidgrowthofdigitaltextdataacrossvariousdomains,includingnewsarticles,socialmediaposts,customerreviews,andscientificliterature.Featureselectionplaysafundamentalroleintextclassificationbyselectingasubsetofrelevantfeaturesfromtheoriginalfeatureset.Thisprocessnotonlyreducesthedimensionalityofthedatabutalsoenhancestheclassificationaccuracybyfocusingonthemostinformativefeatures.Theobjectiveofthispaperistoprovideadetailedanalysisofvariousfeatureselectionalgorithmsandtheirimpactontextclassificationperformance. 2.ImportanceofFeatureSelectioninTextClassification Featureselectionisessentialintextclassificationforseveralreasons: -DimensionalityReduction:Textualdataoftencontainsalargenumberoffeatures,suchaswordsorn-grams.Featureselectionhelpsreducethedimensionalityofthedata,whichalleviatesthecurseofdimensionalityproblemandimprovescomputationalefficiency. -ImprovedClassificationAccuracy:Byselectingthemostrelevantanddiscriminativefeatures,featureselectioncanimprovetheaccuracyoftextclassificationmodels.Irrelevantorredundantfeaturescanintroducenoiseandimpacttheperformanceofclassifiers. -Interpretability:Featureselectionf