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Communication Dans Un Congrès Année : 2018

Generating Term Weighting Schemes through Genetic Programming

Résumé

Term-Weighting Scheme (TWS) is an essential step in text classification. It determines how documents are represented in the Vector Space Model (VSM). Even though state-of-the-art TWSs exhibit good behaviors, a large number of new works propose new approaches and new TWSs that improve performances. Furthermore, it is still difficult to tell which TWS is well suited for a specific problem. In this paper, we are interested in automatically generating new TWSs with the help of evolutionary algorithms and especially genetic programming (GP). GP evolves and combines different statistical information and produces a new TWS based on the performance of the learning method. We experience the generated TWSs on three well-known benchmarks. Our study shows that even early generated formulas are quite competitive with the state-of-the-art TWSs and even in some cases outperform them.
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Dates et versions

hal-01859657 , version 1 (22-08-2018)

Identifiants

  • HAL Id : hal-01859657 , version 1

Citer

Ahmad Mazyad, Fabien Teytaud, Cyril Fonlupt. Generating Term Weighting Schemes through Genetic Programming. The 4th Annual Conference on machine Learning, Optimization and Data science (LOD), Sep 2018, Tuscany, Italy. pp.92-103. ⟨hal-01859657⟩
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