Thispaper is a comparativestudy of feature selectionmethodsintext categorization. Four methods were evaluated, including document frequency ( DF) , information gain ( IG) , mutual information ( MI) andV 2 -test ( CHI). ASupport Vector Machine ( SVM) anda k-nearest neighbor ( KNN) wereselectedastheevaluating class-i fiers. We foundIG, MI andCHI hadpoor performance inour test, thoughthey behavewell inEnglishtext catego-rization. We analyzedthereasonstheoretically andput forwardedthe possible solutions. Afurthermore experiment provedthat the combinedfeatureselectionmethodis effective.

中文文本分类中特征抽取方法的比较研究.pdf

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