A comparison of genetic programming variants for data classification

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

@InProceedings{EEH99b,

• author = "Jeroen Eggermont and Agoston E. Eiben and Jano I. {van Hemert}",
• title = "A comparison of genetic programming variants for data classification",
• booktitle = "Advances in Intelligent Data Analysis, Third International Symposium, IDA-99",
• year = "1999",
• editor = "David J. Hand and Joost N. Kok and Michael R. Berthold",
• volume = "1642",
• series = "LNCS",
• email = "jvhemert@cs.leidenuniv.nl",
• pages = "281--290",
• address = "Amsterdam, The Netherlands",
• publisher_address = "Berlin",
• month = "9--11 " # aug,
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming, classification, data mining",
• URL = "http://www.liacs.nl/~jeggermo/publications/ida99.ps.gz",
• URL = "http://www.vanhemert.co.uk/publications/ida99.A_comparison_of_genetic_programming_variants_for_data_classification.ps.gz",
• ISBN = "3-540-66332-0",
• abstract = "We report a comparative study on different variations of genetic programming applied on binary data classification problems. The first genetic programming variant is weighting data records for calculating the classification error and modifying the weights during the run. Hereby the algorithm is defining its own fitness function in an on-line fashion giving higher weights to `hard' records. Another novel feature we study is the atomic representation, where `Booleanization' of data is not performed at the root, but at the leafs of the trees and only Boolean functions are used in the trees' body. As a third aspect we look at generational and steady-state models in combination of both features.",
• notes = "IDA-99, Booleanization of inputs, ML: Australian credit, German Credit, Heart Disease, Pima. steady state. SAW-ing",

}

Genetic Programming entries for Jeroen Eggermont Gusz Eiben Jano I van Hemert

Citations