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Poster Communications Year : 2017

Learning new Term Weighting Schemes with Genetic Programming

Abstract

Text Classification (or Text Categorization) is a popular machine learning task which consists in assigning categories to documents. Feature weight methods are classic tools that are used in text categorization in order to assign a score to each term of a document based on a mathematical formula. In this paper, we are interested in automatically generating these formulas based on genetic programming. We experiment the generated formulas on three well-known benchmarks and state of the art classifiers.
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Dates and versions

hal-01662138 , version 1 (12-12-2017)

Identifiers

  • HAL Id : hal-01662138 , version 1

Cite

Ahmad Mazyad, Fabien Teytaud, Cyril Fonlupt. Learning new Term Weighting Schemes with Genetic Programming. The Biennial International Conference on Artificial Evolution, Oct 2017, Paris, France. 2017. ⟨hal-01662138⟩
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