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A Novel Statistical Feature Selection Approach for Text Categorization

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초록

For text categorization task, distinctive text features selection is important due to feature space highdimensionality. It is important to decrease the feature space dimension to decrease processing time andincrease accuracy. In the current study, for text categorization task, we introduce a novel statistical featureselection approach. This approach measures the term distribution in all collection documents, the termdistribution in a certain category and the term distribution in a certain class relative to other classes. Theproposed method results show its superiority over the traditional feature selection methods.

Abstract

For text categorization task, distinctive text features selection is important due to feature space highdimensionality. It is important to decrease the feature space dimension to decrease processing time andincrease accuracy. In the current study, for text categorization task, we introduce a novel statistical featureselection approach. This approach measures the term distribution in all collection documents, the termdistribution in a certain category and the term distribution in a certain class relative to other classes. Theproposed method results show its superiority over the traditional feature selection methods.

참고문헌 (45)

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