Application of Genetic Programming to Induction of Linear Classification Trees

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{bot:2000:GPilct,

• author = "Martijn C. J. Bot and William B. Langdon",
• title = "Application of Genetic Programming to Induction of Linear Classification Trees",
• booktitle = "Genetic Programming, Proceedings of EuroGP'2000",
• year = "2000",
• editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty",
• volume = "1802",
• series = "LNCS",
• pages = "247--258",
• address = "Edinburgh",
• publisher_address = "Berlin",
• month = "15-16 " # apr,
• organisation = "EvoNet",
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming: Poster",
• ISBN = "3-540-67339-3",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/bot.eurogp2000.19jan.ps.gz",
• URL = "http://citeseer.ist.psu.edu/318695.html",
• DOI = "doi:10.1007/978-3-540-46239-2_18",
• abstract = "A common problem in datamining is to find accurate classifiers for a dataset. For this purpose, genetic programming (GP) is applied to a set of benchmark classification problems. Using GP we are able to induce decision trees with a linear combination of variables in each function node. A new representation of decision trees using strong typing in GP is introduced. With this representation it is possible to let the GP classify into any number o f classes. Results indicate that GP can be applied successfully to classification problems. Comparisons with current state-of-the-art algorithms in machine learning are presented and areas of future research are identified.",
• notes = "See also \cite{bot:1999:GPilct} EuroGP'2000, part of \cite{poli:2000:GP}",

}

Genetic Programming entries for Martijn C J Bot William B Langdon

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