%0 Conference Paper %1 EEH99b %A Eggermont, Jeroen %A Eiben, Agoston E. %A van Hemert, Jano I. %B Advances in Intelligent Data Analysis, Third International Symposium, IDA-99 %C Amsterdam, The Netherlands %D 1999 %E Hand, David J. %E Kok, Joost N. %E Berthold, Michael R. %I Springer-Verlag %K algorithms, classification, data genetic mining programming, %P 281--290 %T A comparison of genetic programming variants for data classification %U http://www.vanhemert.co.uk/publications/ida99.A_comparison_of_genetic_programming_variants_for_data_classification.ps.gz %V 1642 %X 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. %@ 3-540-66332-0

@inproceedings{EEH99b, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Amsterdam, The Netherlands}, author = {Eggermont, Jeroen and Eiben, Agoston E. and {van Hemert}, Jano I.}, biburl = {https://www.bibsonomy.org/bibtex/22a6ff5aa4a14b60668add91cc7eee70e/brazovayeye}, booktitle = {Advances in Intelligent Data Analysis, Third International Symposium, IDA-99}, editor = {Hand, David J. and Kok, Joost N. and Berthold, Michael R.}, email = {jvhemert@cs.leidenuniv.nl}, interhash = {8a55488ca35a07a806c5813c25e29abc}, intrahash = {2a6ff5aa4a14b60668add91cc7eee70e}, isbn = {3-540-66332-0}, keywords = {algorithms, classification, data genetic mining programming,}, month = {9--11 August}, notes = {IDA-99, Booleanization of inputs, ML: Australian credit, German Credit, Heart Disease, Pima. steady state. SAW-ing}, pages = {281--290}, publisher = {Springer-Verlag}, publisher_address = {Berlin}, series = {LNCS}, timestamp = {2008-06-19T17:39:07.000+0200}, title = {A comparison of genetic programming variants for data classification}, url = {http://www.vanhemert.co.uk/publications/ida99.A_comparison_of_genetic_programming_variants_for_data_classification.ps.gz}, volume = 1642, year = 1999 }