Improving Relevance Measures Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{neshatian:2012:EuroGP,

• author = "Kourosh Neshatian and Mengjie Zhang",
• title = "Improving Relevance Measures Using Genetic Programming",
• booktitle = "Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012",
• year = "2012",
• month = "11-13 " # apr,
• editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta",
• series = "LNCS",
• volume = "7244",
• publisher = "Springer Verlag",
• address = "Malaga, Spain",
• pages = "97--108",
• organisation = "EvoStar",
• isbn13 = "978-3-642-29138-8",
• DOI = "doi:10.1007/978-3-642-29139-5_9",
• keywords = "genetic algorithms, genetic programming, Relevance measure, Binary classification, Multivariate dependency analysis",
• 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.",
• 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

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