- author = "Ahmad Mazyad and Fabien Teytaud and Cyril Fonlupt",
- title = "Learning new Term Weighting Schemes with Genetic Programming",
- booktitle = "Artificial Evolution preprint",
- howpublished = "HAL",
- year = "2017",
- keywords = "genetic algorithms, genetic programming",
- identifier = "hal-01662138",
- language = "en",
- oai = "oai:HAL:hal-01662138v1",
-
URL = "
https://hal.inria.fr/hal-01662138",
-
URL = "https://hal.inria.fr/hal-01662138/document",
-
URL = "
https://hal.inria.fr/hal-01662138/file/pg.pdf",
- size = "12 pages",
- 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.",
-
notes = "Does not appear in Springer, LNCS 10764, EA 2017
https://link.springer.com/book/10.1007/978-3-319-78133-4
also known as \cite{oai:HAL:hal-01662138v1}",
