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Dr. S. M. Kamruzzaman

Assistant Professor

Department of Software Engineering, College of Computer and Information Sciences

علوم الحاسب والمعلومات
Building No. 31, Level 2

ERANN: An algorithm to extract symbolic rules from trained artificial neural networks

Kamruzzaman, S. M. . 2012

This paper presents an algorithm to extract symbolic rules from trained artificial neural networks (ANNs), called ERANN. In many applications, it is desirable to extract knowledge from ANNs for the users to gain a better understanding of how the networks solve the problems. Although ANN usually achieves high classification accuracy, the obtained results sometimes may be incomprehensible, because the knowledge embedded within them is distributed over the activation functions and the connection weights. This problem can be solved by extracting rules from trained ANNs. To do so, a rule extraction algorithm has been proposed in this paper to extract symbolic rules from trained ANNs. A standard three-layer feedforward ANN with four-phase training is the basis of the proposed algorithm. Extensive experimental studies on a set of benchmark classification problems, including breast cancer, iris, diabetes, wine, season, golfplaying, and lenses classification, demonstrates the applicability of the proposed method. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the rules accuracy. The proposed method achieved accuracy values 96.28%, 98.67%, 76.56%, 91.01%, 100%, 100%, and 100% for the above problems, respectively. It has been seen that these results are one of the best results comparing with results obtained from related previous studies.
Publication Work Type
ISI Journal
Volume Number
58
Issue Number
2
Magazine \ Newspaper
IETE Journal of Research
Pages
pp. 138-154
more of publication
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Routing is one of the most important issues in multihop ad hoc networks. In the routing for mobile cognitive radio (CR) networks, the constraints on residual energy of each user and the…

by S. M. Kamruzzaman, Eunhee Kim, Dong Geun Jeong, Wha Sook Jeon
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