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Destructive Algorithm for Rule Extraction based on a Trained Neural Network

by M.e. Elalami
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 21
Year of Publication: 2012
Authors: M.e. Elalami
10.5120/5833-8119

M.e. Elalami . Destructive Algorithm for Rule Extraction based on a Trained Neural Network. International Journal of Computer Applications. 42, 21 ( March 2012), 8-14. DOI=10.5120/5833-8119

@article{ 10.5120/5833-8119,
author = { M.e. Elalami },
title = { Destructive Algorithm for Rule Extraction based on a Trained Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 21 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 8-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number21/5833-8119/ },
doi = { 10.5120/5833-8119 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:31:53.429175+05:30
%A M.e. Elalami
%T Destructive Algorithm for Rule Extraction based on a Trained Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 21
%P 8-14
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The present paper introduces a new destructive algorithm for rule extraction based on a trained neural network. The degree of complexity of neural network increases exponentially as a factor of the numbers of input and hidden nodes. Therefore, the dimensionality of the trained neural network is reduced by using a proposed destructive algorithm to extract only the most effective values of the input attributes which have higher impact on the output result for each class. Thus, the searching efficiency is highly increased and the computation is dramatically reduced for extracting rules. The generated rules from the proposed model are fired through two levels for each class. As for the first level, it deals with each individual effective input value, and the second level is concerned with each possible conjunction of the effective input values. Moreover, the proposed model extracts the strongest rules which represent a large number of instances from the database by adjusting the similarity measure threshold value. Finally, the proposed model is evaluated on different public-domain datasets and compared with standard learning models from WEKA, then the results assert that the set of rules extraction from the proposed method is more accurate and concise compared with those obtained by the other models.

References
  1. Oscar Marbán, Javier Segovia, Ernestina Menasalvas, Covadonga Fernández-Baizán, "Toward data mining engineering: A software engineering approach", Information Systems, Volume 34, Issue 1, March 2009, Pages 87-107.
  2. Ali Buldu, Kerem Üçgün, "Data mining application on students' data", Procedia- Social and Behavioral Sciences, Volume 2, Issue 2, 2010, Pages 5251-5259.
  3. Flora S. Tsai, Agus T. Kwee, "Database optimization for novelty mining of business blogs", Expert Systems with Applications, Volume 38, Issue 9, September 2011, Pages 11040-11047.
  4. Chien-Hsing Wu, Shu-Chen Kao, Yann-Yean Su, Chuan-Chun Wu, "Targeting customers via discovery knowledge for the insurance industry", Expert Systems with Applications, Volume 29, Issue 2, August 2005, Pages 291-299.
  5. Tong-Yan Li, Xing-Ming Li, "Preprocessing expert system for mining association rules in telecommunication networks ", Expert Systems with Applications, Volume 38, Issue 3, March 2011, Pages 1709-1715
  6. Uko Maran, Sulev Sild, Iiris Kahn, Kalev Takkis, "Mining of the chemical information in GRID environment", Future Generation Computer Systems, Volume 23, Issue 1, 1 January 2007, Pages 76-83.
  7. Xuezhong Zhou, Shibo Chen, et el "Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support ", Artificial Intelligence in Medicine, Volume 48, Issues 2-3, February-March 2010, Pages 139-152.
  8. Eyal Kolman, Michael Margaliot, "Extracting symbolic knowledge from recurrent neural networks-A fuzzy logic approach", Fuzzy Sets and Systems, Volume 160, Issue 2, 16 January 2009, Pages 145-161.
  9. Jun Wang, Yunpeng Wu, Xuening Liu, Xiaoying Gao, "Knowledge acquisition method from domain text based on theme logic model and artificial neural network", Expert Systems with Applications, Volume 37, Issue 1, January 2010, Pages 267-275.
  10. C. K. Kwong, K. Y. Chan, Y. C. Tsim, "A genetic algorithm based knowledge discovery system for the design of fluid dispensing processes for electronic packaging", Expert Systems with Applications, Volume 36, Issue 2, Part 2, March 2009, Pages 3829-3838.
  11. Muzaffer Kapanoglu, Mete Alikalfa, "Learning IF–THEN priority rules for dynamic job shops using genetic algorithms", Robotics and Computer-Integrated Manufacturing, Volume 27, Issue 1, February 2011, Pages 47-55.
  12. Leyli Mohammad Khanli, Farnaz Mahan, Ayaz Isazadeh, "Active rule learning using decision tree for resource management in Grid computing", Future Generation Computer Systems, Volume 27, Issue 6, June 2011, Pages 703-710.
  13. Mouloud Boumahdi, Jean-Paul Dron, Saïd Rechak, Olivier Cousinard, "On the extraction of rules in the identification of bearing defects in rotating machinery using decision tree", Expert Systems with Applications, Volume 37, Issue 8, August 2010, Pages 5887-5894.
  14. Francesco Gagliardi, "Instance-based classifiers applied to medical databases: Diagnosis and knowledge extraction", Artificial Intelligence in Medicine, Volume 52, Issue 3, July 2011, Pages 123-139.
  15. Amelia Zafra, Cristóbal Romero, Sebastián Ventura, "Multiple instance learning for classifying students in learning management systems", Expert Systems with Applications, Volume 38, Issue 12, November-December 2011, Pages 15020-15031.
  16. Wouter Verbeke, David Martens, Christophe Mues, Bart Baesens, "Building comprehensible customer churn prediction models with advanced rule induction techniques", Expert Systems with Applications, Volume 38, Issue 3, March 2011, Pages 2354-2364.
  17. Jerzy B?aszczy?ski, Roman S?owi?ski, Marcin Szel?g, "Sequential covering rule induction algorithm for variable consistency rough set approaches", Information Sciences, Volume 181, Issue 5, 1 March 2011, Pages 987-1002.
  18. Nahla Barakat, Andrew P. Bradley, "Rule extraction from support vector machines: A review", Neurocomputing, Volume 74, Issues 1-3, December 2010, Pages 178-190.
  19. M. A. H. Farquad, V. Ravi, S. Bapi Raju, "Support vector regression based hybrid rule extraction methods for forecasting", Expert Systems with Applications, Volume 37, Issue 8, August 2010, Pages 5577-5589.
  20. Humar Kahramanli, Novruz Allahverdi, "Rule extraction from trained adaptive neural networks using artificial immune systems", Expert Systems with Applications 36 (2009) 1513–1522.
  21. LiMin. Fu, "Rule generation from neural networks", IEEE Transactions on Systems, Man and Cybernetics, Vol. 24 No. 8, 1994, pp. 1114-1124.
  22. G. Towell and J. Shavlik, "The extraction of refined rules from knowledge based neural networks", Machine Learning, Vol. 131, 1993, pp. 71-101.
  23. R. Setiono, K. H. Wee, and M. J. Zurada, "Extraction of Rules from artificial neural network for nonlinear regression", IEEE Transaction Neural Networks, Vol. 23 No. 23, 2002, pp. 564-577.
  24. R. Krishnan, G. Sivakumar, and P. Bhattacharya, "A search technique for rule extraction from trained neural networks," Pattern Recognit. Lett. , vol. 20, no. 3, pp. 273–280, Mar. 1999.
  25. G. G. Towell, J. W. Shavlik, and M. O. Noordewier, "Refinement of approximate domain theories by knowledge-based neural networks," in Proc. 8th Nat. Conf. Artif. Intell. , Boston, MA, 1990, pp. 861–866.
  26. S. B. Thrun, "Extracting provably correct rules from neural networks", in Technical Report IAI-TR-93-5, Institut fur Informatik III Universitat Bonn, 1994.
  27. M. W. Craven, "Extracting comprehensible models from trained neural networks", Ph. D. Thesis, University of Wisconsin, Madison, 1996.
  28. Olcay Boz, "Extracting decision tree from trained neural networks", ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002, pp. 456-461.
  29. Z. H. Zhou, Y. Jiang, Y. B. Yang, and S. F. Chen, "Extracting neural networks from trained neural network Ensembles", AI Communications, Vol. 16 No. 1, pp. 3-15, 2003.
  30. A. Garcez, S d'Avila, K. Broda, D. M. Gabbay, "Symbolic knowledge extraction from trained neural networks: A sound approach", Artificial Intelligence, Vol. 125, 2001, pp. 155-207.
  31. Tickle, A. B. , Orlowski, M. , and Diederich, J. , "DEDEC: A Methodology for Extracting Rules from Trained Artificial Neural Networks", Proceedings of The Rule Extraction From Trained Artificial Neural Networks Workshop, 1996.
  32. Rumelhart, D. E. . G. E. Hinton. and R. J. Williams 1986. "Learning internal representations by error propagation", Page 318 in Parallel Distributed Processing: Explorations in the Micro-Structure of Cognition. Vol. 1. D. E. Rumelhart and J. L. McClelland, ed. MIT Press, Cambridge, MA.
  33. C. McMillan, M. C. Mozer, and P. Smolensky, "The connectionist science game: Rule extraction and refinement in a neural network", in Proceedings of the 13th Annual Conference of the Cognitive Science Society, 1991.
  34. R. Setiono and H. Liu, "Understanding neural networks via rule extraction", edited by Chris S. Mellish, in Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, San Mateo, August 20–25, 1995, Morgan Kaufmann, pp. 480–487.
  35. A. B. Tickle, R. Andrews, M. Golea, and J. Diederich, "The Truth Will Come to Light: Directions and Challenges in Extracting the Knowledge Embedded Within Trained Artificial Neural Networks", IEEE Trans. Neural Networks, vol 9, pp. 1057–1068, 1998.
  36. Humar Kahramanli, Novruz Allahverdi, "Rule extraction from trained adaptive neural networks using artificial immune systems", Expert Systems with Applications, Volume 36, Issue 2, Part 1, March 2009, Pages 1513-1522.
  37. K. C. TAN, Q. YU and J. H. ANG, "A co-evolutionary algorithm for rules discovery in data mining", International Journal of Systems Science Vol. 37, No. 12, 10 October 2006, 835–864.
  38. Tom M. Mitchell, "Machine Learning", McGraw-Hill Book Co, Copyright 1997.
  39. S. B. Thrun, "The MONK's problem: A performance comparison of different learning algorithms", Carnegie-Mellon University, Technical Report, 1991.
  40. WEKA at http://www. cs. waikato. ac. nz/~ml/wek
Index Terms

Computer Science
Information Sciences

Keywords

Rule Extraction Supervised Learning Neural Network Destructive Technique Performance Measures.

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