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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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Recursive-Rule Extraction Algorithm With J48graft And Applications To Generating Credit Scores

Yoichi Hayashi / Yuki Tanaka / Tomohiro Takagi / Takamichi Saito / Hideaki Iiduka / Hiroaki Kikuchi / Guido Bologna
  • Department of Information Technology, University of Applied Sciences of Western Switzerland Rue de la prairie 4, 1204 Geneva, Switzerland
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/ Sushmita Mitra
  • Sushmita Mitra Machine Intelligence Unit, Indian Statistical Institute 203 B.T. Road, Kolkata 700 108, India
  • Other articles by this author:
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Published Online: 2016-01-13 | DOI: https://doi.org/10.1515/jaiscr-2016-0004


The purpose of this study was to generate more concise rule extraction from the Recursive-Rule Extraction (Re-RX) algorithm by replacing the C4.5 program currently employed in Re-RX with the J48graft algorithm. Experiments were subsequently conducted to determine rules for six different two-class mixed datasets having discrete and continuous attributes and to compare the resulting accuracy, comprehensibility and conciseness. When working with the CARD1, CARD2, CARD3, German, Bene1 and Bene2 datasets, Re-RX with J48graft provided more concise rules than the original Re-RX algorithm. The use of Re-RX with J48graft resulted in 43.2%, 37% and 21% reductions in rules in the case of the German, Bene1 and Bene2 datasets compared to Re-RX. Furthermore, the Re-RX with J48graft showed 8.87% better accuracy than the Re-RX algorithm for the German dataset. These results confirm that the application of Re-RX in conjunction with J48graft has the capacity to facilitate migration from existing data systems toward new concise analytic systems and Big Data.

Keywords: Rule Extraction; Credit Scoring; Re-RX algorithm; J48graft


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About the article

Published Online: 2016-01-13

Published in Print: 2016-01-01

Citation Information: Journal of Artificial Intelligence and Soft Computing Research, Volume 6, Issue 1, Pages 35–44, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2016-0004.

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© 2016 Academy of Management (SWSPiZ), Lodz. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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