Genetic Programming for Feature Subset Ranking in Binary Classification Problems
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gp-bibliography.bib Revision:1.7964
- @InProceedings{Neshatian:2009:eurogp,
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author = "Kourosh Neshatian and Mengjie Zhang",
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title = "Genetic Programming for Feature Subset Ranking in
Binary Classification Problems",
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booktitle = "Proceedings of the 12th European Conference on Genetic
Programming, EuroGP 2009",
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year = "2009",
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editor = "Leonardo Vanneschi and Steven Gustafson and
Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner",
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volume = "5481",
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series = "LNCS",
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pages = "121--132",
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address = "Tuebingen",
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month = apr # " 15-17",
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organisation = "EvoStar",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Entropy,
Covariance, Remote Sensing, Sonar",
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isbn13 = "978-3-642-01180-1",
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DOI = "doi:10.1007/978-3-642-01181-8_11",
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size = "12 pages",
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abstract = "We propose a genetic programming (GP) system for
measuring the relevance of subsets of features in
binary classification tasks. A virtual program
structure and an evaluation function are defined in a
way that constructed GP programs can measure the
goodness of subsets of features. The proposed system
can detect relevant subsets of features in different
situations including multimodal class distributions and
mutually correlated features where other ranking
methods have difficulties. Our empirical results
indicate that the proposed system is good at ranking
subsets and giving insight into the actual
classification performance. The proposed ranking system
is also efficient in terms of feature selection.",
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notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in
conjunction with EvoCOP2009, EvoBIO2009 and
EvoWorkshops2009",
- }
Genetic Programming entries for
Kourosh Neshatian
Mengjie Zhang
Citations