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Communication Dans Un Congrès Année : 2018

Generating Term Weighting Schemes through Genetic Programming

Résumé

Term-Weighting Scheme (TWS) is an important 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 generates 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-01859681 , version 1 (22-08-2018)

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  • HAL Id : hal-01859681 , version 1

Citer

Ahmad Mazyad, Fabien Teytaud, Cyril Fonlupt. Generating Term Weighting Schemes through Genetic Programming. GECCO 2018, the Genetic and Evolutionary Computation Conference Companion a recombination of the 27th International Conference on Genetic Algorithms (ICGA) and the 23rd Annual Genetic Programming Conference (GP), Jul 2018, Kyoto, Japan. pp.268-269. ⟨hal-01859681⟩
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