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Journal of Computer Science Technology Updates (Volume 1 Issue 1) |
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| Survey on the Family of the Recursive-Rule Extraction Algorithm |  |
| Pages 26-34 Yoichi Hayashi, Tomohiro Takagi, Hiroyuki Mori, Hiroaki Kikuchi, Takamichi Saito, Hideaki Iiduka and Sayaka Akioka DOI: http://dx.doi.org/10.15379/2410-2938.2014.01.01.04Published: 24 December 2014 |
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| Abstract | |
| In this paper, we first review the theoretical and historical backgrounds on rule extraction from neural network ensembles. Because the structures of previous neural network ensembles were quite complicated, research on an efficient rule extraction algorithm from neural network ensembles has been sparse, even though a practical need exists for rule extraction in Big Data datasets. We describe the Recursive-Rule extraction (Re-RX) algorithm, which is an important step toward handling large datasets. Then we survey the family of the Recursive-Rule extraction algorithm, i.e. the Multiple-MLP Ensemble Re-RX algorithm, and present concrete applications in financial and medical domains that require extremely high accuracy for classification rules. Finally, we mention two promising ideas to considerably enhance the accuracy of the Multiple-MLP Ensemble Re-RX algorithm. We also discuss developments in the near future that will make the Multiple-MLP Ensemble Re-RX algorithm much more accurate, concise, and comprehensible rule extraction from mixed datasets. | |
| Keywords | |
| Ensemble Concepts, Rule Extraction, Re-RX Algorithm, Multiple-MLP Ensemble, Neural Network Rule Extraction, Neural Network Ensembles, Data Mining, Ensemble Learning. | |
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