Improving Relevance Measures Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{neshatian:2012:EuroGP,
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author = "Kourosh Neshatian and Mengjie Zhang",
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title = "Improving Relevance Measures Using Genetic
Programming",
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booktitle = "Proceedings of the 15th European Conference on Genetic
Programming, EuroGP 2012",
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year = "2012",
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month = "11-13 " # apr,
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editor = "Alberto Moraglio and Sara Silva and
Krzysztof Krawiec and Penousal Machado and Carlos Cotta",
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series = "LNCS",
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volume = "7244",
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publisher = "Springer Verlag",
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address = "Malaga, Spain",
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pages = "97--108",
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organisation = "EvoStar",
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isbn13 = "978-3-642-29138-8",
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DOI = "doi:10.1007/978-3-642-29139-5_9",
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keywords = "genetic algorithms, genetic programming, Relevance
measure, Binary classification, Multivariate dependency
analysis",
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abstract = "Relevance is a central concept in many feature
selection algorithms. Given a relevance measure, a
feature selection algorithm searches for a subset of
features that maximise the relevance between the subset
and target concepts. This paper first shows how
relevance measures that rely on the posterior
estimation such as information theory measures may fail
to quantify the actual utility of subsets of features
in certain situations. The paper then proposes a
solution based on Genetic Programming which can improve
the usability of these measures. The paper is focused
on classification problems with numeric features.",
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notes = "Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in
conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012
and EvoApplications2012",
- }
Genetic Programming entries for
Kourosh Neshatian
Mengjie Zhang
Citations