Evolving Rules for Document Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{eurogp:HirschSH05,

• author = "Laurence Hirsch and Masoud Saeedi and Robin Hirsch",
• editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini",
• title = "Evolving Rules for Document Classification",
• booktitle = "Proceedings of the 8th European Conference on Genetic Programming",
• publisher = "Springer",
• series = "Lecture Notes in Computer Science",
• volume = "3447",
• year = "2005",
• address = "Lausanne, Switzerland",
• month = "30 " # mar # " - 1 " # apr,
• organisation = "EvoNet",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-25436-6",
• pages = "85--95",
• DOI = "doi:10.1007/978-3-540-31989-4_8",
• DOI = "doi:10.1007/b107383",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• abstract = "We describe a novel method for using Genetic Programming to create compact classification rules based on combinations of N-Grams (character strings). Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also suggest that because the induced rules are meaningful to a human analyst they may have a number of other uses beyond classification and provide a basis for text mining applications.",
• notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005",

}

Genetic Programming entries for Laurence Hirsch Masoud Saeedi Robin Hirsch

Citations