Increasing Rule Extraction Accuracy by Post-Processing GP Trees

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Johansson:2008:cec,

• author = "Ulf Johansson and Rikard Konig and Tuve Lofstrom and Lars Niklasson",
• title = "Increasing Rule Extraction Accuracy by Post-Processing GP Trees",
• booktitle = "2008 IEEE World Congress on Computational Intelligence",
• year = "2008",
• editor = "Jun Wang",
• pages = "3005--3010",
• address = "Hong Kong",
• month = "1-6 " # jun,
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• isbn13 = "978-1-4244-1823-7",
• file = "EC0669.pdf",
• DOI = "doi:10.1109/CEC.2008.4631203",
• abstract = "Genetic programming (GP), is a very general and efficient technique, often capable of outperforming more specialised techniques on a variety of tasks. In this paper, we suggest a straightforward novel algorithm for post-processing of GP classification trees. The algorithm iteratively, one node at a time, searches for possible modifications that would result in higher accuracy. More specifically, the algorithm for each split evaluates every possible constant value and chooses the best. With this design, the post-processing algorithm can only increase training accuracy, never decrease it. In this study, we apply the suggested algorithm to GP trees, extracted from neural network ensembles. Experimentation, using 22 UCI datasets, shows that the post-processing results in higher test set accuracies on a large majority of datasets. As a matter of fact, for two setups of three evaluated, the increase in accuracy is statistically significant.",
• keywords = "genetic algorithms, genetic programming",
• notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.",

}

Genetic Programming entries for Ulf Johansson Rikard Konig Tuve Lofstrom Lars Niklasson

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