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Using Imaginary Ensembles to Select GP Classifiers
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
2010 (English)In: Genetic Programming: 13th European Conference, EuroGP 2010, Istanbul, Turkey, April 7-9, 2010, Proceedings / [ed] A.I. et al. Esparcia-Alcazar, Springer-Verlag Berlin Heidelberg , 2010, 278-288 p.Conference paper (Refereed)
Abstract [en]

When predictive modeling requires comprehensible models, most data miners will use specialized techniques producing rule sets or decision trees. This study, however, shows that genetically evolved decision trees may very well outperform the more specialized techniques. The proposed approach evolves a number of decision trees and then uses one of several suggested selection strategies to pick one specific tree from that pool. The inherent inconsistency of evolution makes it possible to evolve each tree using all data, and still obtain somewhat different models. The main idea is to use these quite accurate and slightly diverse trees to form an imaginary ensemble, which is then used as a guide when selecting one specific tree. Simply put, the tree classifying the largest number of instances identically to the ensemble is chosen. In the experimentation, using 25 UCI data sets, two selection strategies obtained significantly higher accuracy than the standard rule inducer J48.

Place, publisher, year, edition, pages
Springer-Verlag Berlin Heidelberg , 2010. 278-288 p.
, LNCS, 6021
Keyword [en]
classification, decision trees, ensembles, genetic programming, Machine learning
National Category
Computer Science Information Systems
URN: urn:nbn:se:hb:diva-6401Local ID: 2320/6793ISBN: 978-3-642-12147-0OAI: diva2:887089


This work was supported by the INFUSIS project (

infusis) at the University of Skövde, Sweden, in partnership with the Swedish Knowledge

Foundation under grant 2008/0502.

Available from: 2015-12-22 Created: 2015-12-22

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Johansson, UlfKönig, RikardLöfström, Tuve
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