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A Comparative Study on Term Weighting Schemes for Text Classification

Abstract : Text Classification (or Text Categorization) is a popular machine learning task. It consists in assigning categories to documents. In this paper, we are interested in comparing state of the art classifiers and state of the art feature weights. Feature weight methods are classic tools that are used in text categorization. We extend previous studies by evaluating numerous term weighting schemes for state of the art classification methods. We aim at providing a complete survey on text classification for fair benchmark comparisons.
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Submitted on : Tuesday, December 12, 2017 - 5:17:44 PM
Last modification on : Thursday, November 25, 2021 - 8:22:29 AM


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  • HAL Id : hal-01662131, version 1



Ahmad Mazyad, Fabien Teytaud, Cyril Fonlupt. A Comparative Study on Term Weighting Schemes for Text Classification. The Third International Conference on Machine Learning, Optimization and Big Data, Sep 2017, Tuscany, Italy. ⟨hal-01662131⟩



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