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• W3204836995 endingPage "115985" @default.
• W3204836995 startingPage "115985" @default.
• W3204836995 abstract "Term weighting scheme has significant effects on the text classification performance. The main reason is that in text classification tasks, term weighting scheme determines the way in which texts are represented in the vector space model. Currently, term frequency-inverse document frequency is the most widely utilized term weighting scheme but it does not use the available category information of the training texts. Taking this resource of category information (or category factor) into account in the study, an improved supervised term weighting method for representing text is developed, which combines a new measure of information namely cumulative residual entropy and the proportional distortion function. To verify the text classification performance of our proposed scheme, we conducted an extensive experimental comparison of proposed scheme with existing schemes on two corpora (i.e., Reuters-21578 and 20 Newsgroups datasets) with different characteristics. Results explicitly show that our proposed scheme can obtain significantly better effect for text classification than others. Specifically, when linear support vector machine classifier is run, performances were improved to 0.972 and 0.833 (micro-F1) on Reuters-21578 dataset and 20 Newsgroups dataset, respectively." @default.
• W3204836995 created "2021-10-11" @default.
• W3204836995 creator A5029225084 @default.
• W3204836995 creator A5038805449 @default.
• W3204836995 creator A5063891789 @default.
• W3204836995 date "2022-03-01" @default.
• W3204836995 modified "2023-09-27" @default.
• W3204836995 title "An improved supervised term weighting scheme for text representation and classification" @default.
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• W3204836995 doi "https://doi.org/10.1016/j.eswa.2021.115985" @default.
• W3204836995 hasPublicationYear "2022" @default.
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