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Extracting Classification Rules with Support Rough Neural Networks

  • He Ming
  • Feng Boqin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3558)

Abstract

Classification is an important theme in data mining. Rough sets and neural networks are two technologies frequently applied to data mining tasks. Integrating the advantages of two approaches, this paper presents a hybrid system to extract efficiently classification rules from a decision table. The neural network system and rough set theory are completely integrated to into a hybrid system and use cooperatively for classification support. Through rough set approach a decision table is first reduced by removing redundant attributes without any classification information loss. Then a rough neural network is trained to extract the rules set form the reduced decision table. Finally, classification rules are generated from the reduced decision table by rough neural network. In addition, a new algorithm of finding a reduct and a new algorithm of rule generation from a decision table are also proposed. The effectiveness of our approach is verified by the experiments comparing with traditional rough set approach.

Keywords

Classification Rule Decision Table Sigmoid Transfer Function Output Layer Neuron Redundant Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Chen, M., Han, J., Yu, P.: Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Date Engineering 8(6), 866–883 (1996)CrossRefGoogle Scholar
  2. 2.
    Bengio, Y., Buhlmann, J., Embrechts, M., Zurada, J.: Introduction to the special issue on neural networks for data mining and knowledge discovery. IEEE Transactions on Neural Networks 11(3), 545–549 (2000)CrossRefGoogle Scholar
  3. 3.
    Ziarko, W.: Introduction to the special issue on rough sets and knowledge discovery. Computational Intelligence 11(2), 223–226 (1995)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Yahia, M., Mahmod, R., Sulaiman, N., Ahmad, F.: Rough neural expert systems. Expert Systems with Applications 18(2), 87–99 (2000)CrossRefGoogle Scholar
  5. 5.
    Phuong, N., Phong, L., Santiprabhob, P., Baets, B.: Approach to generation rules for expert systems using rough set theory. In: IFSA World Congress and 20th NAFIPS International Conference, pp. 877–882 (2001)Google Scholar
  6. 6.
    Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W.: Rough sets. Communications of the ACM 38(11), 88–95 (1995)CrossRefGoogle Scholar
  7. 7.
    Bazan, J., Skowron, A., Synak, P.: Dynamic reducts as a tool for extracting laws from decisions tables. In: Proceedings of the Symposium on Methodologies for Intelligent Systems, pp. 346–355 (1994)Google Scholar
  8. 8.
    Lu, H., Setiono, R., Liu, H.: Effective data mining using neural networks. IEEE Transactions on Knowledge and Data Engineering 8(6), 957–961 (1996)CrossRefGoogle Scholar
  9. 9.
    Craven, M., Shavlik, J.: Using neural networks for data mining. Future Generation Computer Systems 13, 211–229 (1997)CrossRefGoogle Scholar
  10. 10.
    Lingras, P.J.: Rough neural networks. In: Proceedings of the 6th International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU 1996), Granada, Spain, pp. 1445–1450 (1996)Google Scholar
  11. 11.
    Peters, J.F., Skowron, A., Han, L., Ramanna, S.: Towards rough neural computing based on rough membership functions: theory and application. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 572–579. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  12. 12.
    Peters, J.F., Pedrycz, W.: Software Engineering: An Engineering Approach. Wiley, J. & Sons, New York (2000)Google Scholar
  13. 13.
    Pawlak, Z., Skowron, A.: Rough membership functions. In: Yager, R., Fedrizzi, M., Kacprzyk, J. (eds.) Advances in the Dempster-Shafter Theory of Evidence, pp. 251–271. Wiley, J. & Sons, New York (1994)Google Scholar
  14. 14.
    Peters, J.F., Han, L., Ramanna, S.: Rough neural computing in signal analysis. Computational Intelligence 17(3), 493–513 (2001)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Murphy, P.M., Aha, D.W.: UCI repository of machine learning databases, machinereadable data repository. In: Department of Information and Computer Science, Irvine, CA, University of California, Berkeley (1992)Google Scholar
  16. 16.
    Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recognition Letters 24, 833–849 (2003)zbMATHCrossRefGoogle Scholar
  17. 17.
    Chen, X., Zhu, S., Ji, Y.: Entropy based uncertainty measures for classification rules with inconsistency tolerance. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 2816–2821 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • He Ming
    • 1
  • Feng Boqin
    • 1
  1. 1.Department of Computer Science and TechnologyXi’an Jiaotong UniversityXi’anChina

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