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Extracting the Classification Rules from General Fuzzy Min-Max Neural Network

by S. V. Shinde, U. V. Kulkarni, A. N. Chaudhary
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 121 - Number 23
Year of Publication: 2015
Authors: S. V. Shinde, U. V. Kulkarni, A. N. Chaudhary
10.5120/21837-4095

S. V. Shinde, U. V. Kulkarni, A. N. Chaudhary . Extracting the Classification Rules from General Fuzzy Min-Max Neural Network. International Journal of Computer Applications. 121, 23 ( July 2015), 1-7. DOI=10.5120/21837-4095

@article{ 10.5120/21837-4095,
author = { S. V. Shinde, U. V. Kulkarni, A. N. Chaudhary },
title = { Extracting the Classification Rules from General Fuzzy Min-Max Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 121 },
number = { 23 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume121/number23/21837-4095/ },
doi = { 10.5120/21837-4095 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:09:10.721313+05:30
%A S. V. Shinde
%A U. V. Kulkarni
%A A. N. Chaudhary
%T Extracting the Classification Rules from General Fuzzy Min-Max Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 121
%N 23
%P 1-7
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The general fuzzy min-max neural network (GFMMN) is capable to perform the classification as well as clustering of the data. In addition to this it has the ability of learning in a very few passes with a very short training time. But like other artificial neural networks, GFMMN is also like a black box and expressed in terms of min-max values and associated class label. So the justification of classification results given by GFMMN is required to be obtained to make it more adaptive to the real world applications. This paper proposes the model to extract classification rules from trained GFMMN. These rules justify the classification decision given by GFMMN. For this GFMMN is trained for the appropriate value of . The min-max values of all the hyperboxes are quantized and these are expresses in the form of rules. Each rule represent the the kind of patteres falling in that hyperbox. These rules are readable and represents the trained network. Experiments are conducted on eight different benchmark datasets obtained from UCI machine learning repository. These results prove the applicability of the proposed method.

References
  1. J. Han, M. Y. Kamber, and S. C. Lee, Data mining:concepts and techniques, San Francisco, CA, USA: Morgan Kaufmann, 2001.
  2. W. Chen and Y. K. Du, Using neural networks and data mining techniques for the financial distress prediction model, Expert Systems with Applications, vol. 36, pp. 40754086, 2009.
  3. C. Lin, I. Chung and C. Chen, "An entropy-based quantum neuro-fuzzy inference system for classification applications," Neurocomputing, vol. 70, pp. 2502-2516, 2007.
  4. O. Cordon, M. Jesus and F. Herrera, "A proposal on reasoning methods in fuzzy rule-based classification systems," International Journal of Approximate Reasoning, vol. 20, pp. 21-45, 1999.
  5. N. K. Patil, V. S. Malemath, and R. M. Yadahalli, "Color and Texture Based Identification and Classification of food Grains using different Color Models and Haralick features," Int. J. Computer Science & Engineering, ol. 2, no. 12, pp. 3669- 3680, 2011.
  6. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification, second ed. , Wiley, 2001.
  7. P. Langely, W. Iba, and K. Thompson, "An analysis of bayesian classifiers,"AAAI-92 Proceedings, pp. 223-228, 1992.
  8. Decision Tree, Wikipedia, the free encyclopaedia 2005: htttp://en. wikipedia. org/wiki/Decisiontree.
  9. The federal budget execution process decision tree, 2005: http://www. knownet. hhs. gov.
  10. J. R. Quinlan, C4. 5: Programs for machine learning, Morgan Kaufmann, San Mateo, CA. , 1993.
  11. M. Umano, H. Okamoto, I. Hatono, H. Tamura, F. Kawachi, S. Umedzu, and J. Kinoshita, "Fuzzy decision trees by fuzzy ID3 algorithm and its application to diagnosis systems,"3rd IEEE conf on Fuzzy Systems, pp. 2113-2118, 1994.
  12. R. L. Chang and T. Pavlidis, "Fuzzy decision tree algorithms," IEEE Trans. on Systems, Man, and Cybernetics, vol. 7, no. 1, pp. 28-35, 1977.
  13. X. Z. Wang, D. S. Yeung, and E. C. C. Tsang, "A comparative study on heuristic algorithms for generating fuzzy decision trees," IEEE Trans. Systems, Man and Cybernetics, vol. 31, no. 2, pp. 215-226, 2001.
  14. S. Abe, Pattern classification: Neuro-fuzzy methods and their comparison, Springer Verlag, 2001.
  15. Zak, "Neural model of underwater vehicle dynamics," J. of Mathematics and Computers in Simulation, vol. 1, no. 2, pp. 189-195, 2007.
  16. L. A. Zadeh, Fuzzy logic, neural network and soft computing, Commun. ACM, vol. 37, pp. 77-84, 1994.
  17. J. Zupan, Introduction to artificial neural network (ANN) methods: What they are and how to use them, Department of Chemistry, University Rovira Virgili, Tarragona, Spain Acta Chimica Slovenica, pp. 327-352, 1994.
  18. G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen, Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps, IEEE Trans. Neural Networks, vol. 3, pp. 698713, 1992.
  19. S. C. Newton, S. Pemmaraju, and S. Mitra, Adaptive fuzzy leader clustering of complex data sets in pattern recognition, IEEE Trans. Neural Networks, vol. 3, pp. 794800, Sept. 1992.
  20. P. K. Simpson, "Fuzzy min-max neural network - Part I: classification," IEEE Trans. Neural Networks, vol. 3, pp. 776-786, 1992.
  21. A. Joshi, N. Ramakrishman, E. N. Houstis, and J. R. Rice,"On neurobiological, neuro-fuzzy, machine learning, and statistical pattern recognition techniques," IEEE Trans. Neural Networks, vol. 8, no. 1, pp. 18-31, Jan. 1997.
  22. A. V. Nandedkar, "An interactive shadow detection and removal tool using granular reflex fuzzy min-max neural network," in Proc. World Congr. Eng. , vol. 2, 2012.
  23. M. Seera, C. P. Lim, D. Ishak, and H. Singh, "Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMMCART model," IEEE Trans. Neural Networks and Learn. Syst. , vol. 23, no. 1, pp. 97-108, Jan. 2012.
  24. N. P. Jawarkar, R. S. Holambe, and T. K. Basu, "Use of fuzzy min-max neural network for speaker identification," in Proc. ICRTIT, pp. 18-182, 2011.
  25. P. K. Simpson, "Fuzzy min-max neural network - Part II: clustering," IEEE Trans. Fuzzy System, vol. 1, pp. 32-45, 1993.
  26. B. Gabrys and A. Bargiela, "General fuzzy min-max neural network for clustering and classification," IEEE Trans. Neural Networks, vol. 11, pp. 769-783, 2000.
  27. U. V. Kulkarni, T. R. Sontakke, and G. D. Randale, "Fuzzy hyperline segment neural network for rotation invariant handwritten character recognition," in Proc. Int. Joint Conf. on Neural Networks: IJCNN'01, Washington DC, USA, pp. 2918-2923, July 2001.
  28. U. V. Kulkarni and T. R. Sontakke, "Fuzzy hypersphere neural network classifier," in Proc. IEEE Conference on Fuzzy Systems held at University of Melbourne, Australia, December 2001.
  29. A. Rizzi, M. Panella, and F. M. F. Mascioli, A recursive algorithm for fuzzy min-max networks, in Proc. IEEE/INNS/ENNS Int. Joint Conf. Neural Netw. , vol. 6. Como, Italy, Jul. 2000, pp. 541546.
  30. A. Rizzi, F. M. F. Mascioli, and G. Martinelli, Generalized min-max classifier, in Proc. 9th IEEE Int. Conf. Fuzzy Syst. , vol. 1. San Antonio, TX, May 2000, pp. 3641.
  31. A. Rizzi, M. Panella, and F. M. F. Massciloi, Adaptive resolution min-max classifiers, IEEE Trans. Neural Netw. , vol. 13, no. 2, pp. 402414, Mar. 2002.
  32. M. Meneganti, F. S. Saviello, and R. Tagliaferri, Fuzzy neural networks for classification and detection of anomalies, IEEE Trans. Neural Netw. , vol. 9, no. 5, pp. 846861, Sep. 1998.
  33. R. Tagliaferri, A. Eleuteri, M. Meneganti, and F. Barone, Fuzzy min-max neural networks: From classification to regression, Soft Comput. , vol. 5, no. 6, pp. 6976, Feb. 2001.
  34. H. J. Kim, J. Lee, and H. S. Yang, A weighted FMM neural network and its application to face detection, in Neural Information Processing New York, NY, USA: Springer-Verlag, pp. 177186, 2006.
  35. A. Bargiela, W. Pedrycz, and M. Tanaka, "Exclusion/ inclusion fuzzy classification network," J. Knowledge- Based Intelligent Information and Engineering Systems, vol. 2773, pp. 1236-1241, 2003.
  36. A. V. Nandedkar and P. K. Biswas, "A fuzzy min-max neural network classifier with compensatory neuron architecture," IEEE Trans. Neural Networks, vol. 18, pp. 42-54, 2007.
  37. H. Zhang, J. Liu, D. Ma, and Z. Wang, "Data-core-based fuzzy minmax neural network for pattern classification," IEEE Trans. Neural Networks, vol. 22, no. 12, pp. 2339-2352, Dec. 2011.
  38. Reza Davtalab, Mir Hossein Dezfoulian, and Muharram Mansoorizadeh, "Multi-level fuzzy min-max neural network classifier," IEEE Trans. Neural Networks, vol. 25, no. 3, pp. 470- 482, March 2014.
  39. W. Duch, R. Adamczak, and K. Grabczwski, "A new methodology of extraction, optimization and application of crisp and fuzzy logical rules," IEEE Trans. Neural Networks, vol. 12, pp. 277-306, 2001.
  40. S. H. Huang and H. Xing, "Extract intelligible and concise fuzzy rules from neural networks,"Fuzzy Sets and Systems, vol. 132, pp. 233-243, 2002.
  41. UCI repository of machine learning databases, University of California, Irvine, http://www. ics. uci. edu/mlearn/MLRepository. html, 1998.
Index Terms

Computer Science
Information Sciences

Keywords

Neural Network Bayesian Classifiers Fuzzy Systems Support Vector Machines

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