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- Johansson, Ulf, et al.
(författare)
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Increasing Rule Extraction Accuracy by Post-processing GP Trees
- 2008
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Ingår i: Proceedings of the Congress on Evolutionary Computation. - : IEEE. - 9781424418237 - 9781424418220 ; , s. 3010-3015
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 2. |
- König, Rikard, et al.
(författare)
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Using Genetic Programming to Increase Rule Quality
- 2008
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Ingår i: Proceedings of the Twenty-First International FLAIRS Conference (FLAIRS 2008). - : AAAI Press. - 9781577353652 ; , s. 288-293
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Konferensbidrag (refereegranskat)abstract
- Rule extraction is a technique aimed at transforming highly accurate opaque models like neural networks into comprehensible models without losing accuracy. G-REX is a rule extraction technique based on Genetic Programming that previously has performed well in several studies. This study has two objectives, to evaluate two new fitness functions for G-REX and to show how G-REX can be used as a rule inducer. The fitness functions are designed to optimize two alternative quality measures, area under ROC curves and a new comprehensibility measure called brevity. Rules with good brevity classifies typical instances with few and simple tests and use complex conditions only for atypical examples. Experiments using thirteen publicly available data sets show that the two novel fitness functions succeeded in increasing brevity and area under the ROC curve without sacrificing accuracy. When compared to a standard decision tree algorithm, G-REX achieved slightly higher accuracy, but also added additional quality to the rules by increasing their AUC or brevity significantly.
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