Using imaginary ensembles to select GP classifiers

Authors:
Ulf Johansson;Rikard König;Tuve Löfström;Lars Niklasson
Affiliations:
School of Business and Informatics, University of Borås, Sweden;School of Business and Informatics, University of Borås, Sweden;School of Business and Informatics, University of Borås, Sweden;Informatics Research Centre, University of Skövde, Sweden
Venue:
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Year:
2010

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Abstract

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.