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Increasing rule extraction accuracy by post-processing GP trees

8 years 9 months ago
Increasing rule extraction accuracy by post-processing GP trees
—Genetic programming (GP), is a very general and efficient technique, often capable of outperforming more specialized 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.
Ulf Johansson, Rikard König, Tuve Löfstr
Added 29 May 2010
Updated 29 May 2010
Type Conference
Year 2008
Where CEC
Authors Ulf Johansson, Rikard König, Tuve Löfström, Lars Niklasson
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