A Multi-dimensional Genetic Programming Approach for Multi-class Classification Problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{ingalalli:2014:EuroGP,
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author = "Vijay Ingalalli and Sara Silva and Mauro Castelli and
Leonardo Vanneschi",
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title = "A Multi-dimensional Genetic Programming Approach for
Multi-class Classification Problems",
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booktitle = "17th European Conference on Genetic Programming",
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year = "2014",
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editor = "Miguel Nicolau and Krzysztof Krawiec and
Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and
Juan J. Merelo and Victor M. {Rivas Santos} and
Kevin Sim",
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series = "LNCS",
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volume = "8599",
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publisher = "Springer",
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pages = "48--60",
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address = "Granada, Spain",
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month = "23-25 " # apr,
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organisation = "EvoStar",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-662-44302-6",
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DOI = "doi:10.1007/978-3-662-44303-3_5",
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abstract = "Classification problems are of profound interest for
the machine learning community as well as to an array
of application fields. However, multi-class
classification problems can be very complex, in
particular when the number of classes is high. Although
very successful in so many applications, GP was never
regarded as a good method to perform multi-class
classification. In this work, we present a novel
algorithm for tree based GP, that incorporates some
ideas on the representation of the solution space in
higher dimensions. This idea lays some foundations on
addressing multi-class classification problems using
GP, which may lead to further research in this
direction. We test the new approach on a large set of
benchmark problems from several different sources, and
observe its competitiveness against the most successful
state-of-the-art classifiers.",
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notes = "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in
conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014
and EvoApplications2014",
- }
Genetic Programming entries for
Vijay Ingalalli
Sara Silva
Mauro Castelli
Leonardo Vanneschi
Citations