Evolving Rules for Document Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{eurogp:HirschSH05,
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author = "Laurence Hirsch and Masoud Saeedi and Robin Hirsch",
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editor = "Maarten Keijzer and Andrea Tettamanzi and
Pierre Collet and Jano I. {van Hemert} and Marco Tomassini",
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title = "Evolving Rules for Document Classification",
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booktitle = "Proceedings of the 8th European Conference on Genetic
Programming",
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publisher = "Springer",
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series = "Lecture Notes in Computer Science",
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volume = "3447",
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year = "2005",
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address = "Lausanne, Switzerland",
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month = "30 " # mar # " - 1 " # apr,
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organisation = "EvoNet",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "3-540-25436-6",
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pages = "85--95",
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DOI = "doi:10.1007/978-3-540-31989-4_8",
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DOI = "doi:10.1007/b107383",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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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.",
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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