A New Neural Data Analysis Approach Using Ensemble Neural Network Rule Extraction
In this paper, we propose the Ensemble-Recursive-Rule eXtraction (E-Re-RX) algorithm, which is a rule extraction method from ensemble neural networks. We demonstrate that the use of ensemble neural networks produces higher recognition accuracy than individual neural networks and the extracted rules are more comprehensible. E-Re-RX algorithm is an effective rule extraction algorithm for dealing with data sets that mix discrete and continuous attributes. In this algorithm, primary rules are generated as well as secondary rules to handleonlythoseinstances that do not satisfy the primary rules, and then these rules are integrated. We show that this reduces the complexity of using multiple neural networks. This method achieves extremely high recognition rates, even with multiclass problems.
KeywordsEnsemble neural network rule extraction Re-Rx Algorithm Ensemble method Recursive neural network rule extraction
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- 2.Freund, Y., Schapire, R.: Experiments with a new boosting algorithm. In: Proc. of the Thirteenth International Conference on Machine Learning, Bari, Italy, pp. 148–156 (1996)Google Scholar
- 4.Hartono, P.: Ensemble of linear experts as an interpretable piecewise linear classifier. ICIC Exp. Lett. 2, 295–303 (2008)Google Scholar
- 7.Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press (1995)Google Scholar
- 9.Akhand, M.A.H., Murase, K.: Neural Network Ensembles. Lambert Academic Publishing (LAP) (2010)Google Scholar
- 10.Alpaydin, E.: Multiple Neural Networks and Weighted Voting. IEEE Trans. on Pattern Recognition 2, 29–32 (1992)Google Scholar
- 11.University of California, Irvine Machine Learning Repository, http://archive.ics.uci.edu/ml/
- 12.Hara, A., Hayashi, Y.: Ensemble neural network rule extraction using Re-RX algorithm. In: Proc. of IJCNN 2012 (2012) (under review)Google Scholar