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This paper mainly discusses the use of mutual information (MI) and Support Vector Machines (SVMs) for Uyghur Web text classification and digital forensics process of web text categorization: automatic classification and identification, conversion and pretreatment of plain text based on encoding features of various existing Uyghur Web documents etc., introduces the pre-paratory work for Uyghur Web text encoding. Focusing on the non-Uyghur characters and stop words in the web texts filtering, we put forward a Multi-feature Space Normalized Mutual Information (M-FNMI) algorithm and replace MI between single feature and category with mutual information (MI) between input feature combination and category so as to extract more accurate feature words; finally, we classify features with support vector machine (SVM) algorithm. The experimental result shows that this scheme has a high precision of classification and can provide criterion for digital forensics with specific purpose.

Thispapermainlydiscussestheuseofmutualinformation(MI)andSupportVectorMachines(SVMs) forUyghurWebtextclassificationanddigitalforensicsprocessofwebtextcategorization:automatic classificationandidentification,conversionandpretreatmentofplaintextbasedonencodingfeatures ofvariousexistingUyghurWebdocumentsetc.,introducesthepre-paratoryworkforUyghurWeb textencoding.Focusingonthenon-Uyghurcharactersandstopwordsinthewebtextsfiltering, weputforwardaMulti-featureSpaceNormalizedMutualInformation(M-FNMI)algorithmand replaceMIbetweensinglefeatureandcategorywithmutualinformation(MI)betweeninputfeature combinationandcategorysoastoextractmoreaccuratefeaturewords;finally,weclassifyfeatures withsupportvectormachine(SVM)algorithm.Theexperimentalresultshowsthatthisschemehas ahighprecisionofclassificationandcanprovidecriterionfordigitalforensicswithspecificpurpose.

This paper mainly discusses the use of mutual information (MI) and Support Vector Machines (SVMs) for Uyghur Web text classification and digital forensics process of web text categorization: automatic classification and identification, conversion and pretreatment of plain text based on encoding features of various existing Uyghur Web documents etc., introduces the pre-paratory work for Uyghur Web text encoding. Focusing on the non-Uyghur characters and stop words in the web texts filtering, we put forward a Multi-feature Space Normalized Mutual Information (M-FNMI) algorithm and replace MI between single feature and category with mutual information (MI) between input feature combination and category so as to extract more accurate feature words; finally, we classify features with support vector machine (SVM) algorithm. The experimental result shows that this scheme has a high precision of classification and can provide criterion for digital forensics with specific purpose.

No abstract