Structural Risk Minimization on Decision Trees Using An Evolutionary Multiobjective Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{kim:2004:eurogp,

• author = "DaeEun Kim",
• title = "Structural Risk Minimization on Decision Trees Using An Evolutionary Multiobjective Optimization",
• booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings",
• year = "2004",
• editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule",
• volume = "3003",
• series = "LNCS",
• pages = "338--348",
• address = "Coimbra, Portugal",
• publisher_address = "Berlin",
• month = "5-7 " # apr,
• organisation = "EvoNet",
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming: Poster",
• ISBN = "3-540-21346-5",
• DOI = "doi:10.1007/978-3-540-24650-3_32",
• abstract = "Inducing decision trees is a popular method in machine learning. The information gain computed for each attribute and its threshold helps finding a small number of rules for data classification. However, there has been little research on how many rules are appropriate for a given set of data. An evolutionary multi-objective optimisation approach with genetic programming will be applied to the data classification problem in order to find the minimum error rate for each size of decision trees. Following structural risk minimisation suggested by Vapnik, we can determine a desirable number of rules with the best generalisation performance. A hierarchy of decision trees for classification performance can be provided and it is compared with C4.5 application.",
• notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004",

}

Genetic Programming entries for DaeEun Kim

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