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Trading off between Misclassification, Recognition and Generalization in Data Mining with Continuous Features

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Developments in Applied Artificial Intelligence (IEA/AIE 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2358))

Abstract

This paper aims at developing a data mining approach for classification rule representation and automated acquisition from numerical data with continuous attributes. The classification rules are crisp and described by ellipsoidal regions with different attributes for each individual rule. A regularization model trading off misclassification rate, recognition rate and generalization ability is presented and applied to rule refinement. A regularizing data mining algorithm is given, which includes self-organizing map network based clustering techniques, feature selection using breakpoint technique, rule initialization and optimization, classifier structure and usage. An Illustrative example demonstrates the applicability and potential of the proposed techniques for domains with continuous attributes.

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References

  1. R. L. Kennedy, Y. Lee, B. V. Roy, C. D. Reed and R. P. Lippmann, Solvong Data Mining Problems Through Pattern Recognition. Prentice Hall, PTR, Unica Technologies, Inc., (1998)

    Google Scholar 

  2. S. Theodoridis and K. Koutroumbas: Pattern Recognition, Academic Press, (1999)

    Google Scholar 

  3. A.K. Jain, P. W. Duin, and J. Mao, Statistical pattern recognition: a review, IEEE Trans.On Pattern Analysis and Machine Intelligence, 5 (2000) 4–37

    Article  Google Scholar 

  4. S. Sestito and T. S. Dillon, Automated Knowledge Acquisition. Australia: Prentice Hall, (1994)

    MATH  Google Scholar 

  5. T. Kohonen, Self-Organization and Associative Memory. Berlin: Springer-Verlag (1989)

    Google Scholar 

  6. R. P. Lippmann, Pattern classification using neural networks, IEEE Communications Magazine, (1989) 47–64

    Google Scholar 

  7. J. Y. Ching, A. K. C. Wong and K. C. C. Chan, Class-dependent discretization for inductive learning from continuous and mixed-mode data, IEEE Trans.On Pattern Analysis and Machine Intelligence, 7(1995) 641–651

    Article  Google Scholar 

  8. P. K. Simpson, Fuzzy min-max neural networks-Part I: Classification, IEEE Trans. On Neural Networks, 5(1992) 776–786

    Article  Google Scholar 

  9. X. Wu, Fuzzy interpretation of discretized intervals, IEEE Trans. On Fuzzy Systems, 6(1999)753–759

    Google Scholar 

  10. S. Mitra, R. K. De and S. K. Pal, Knowledge-based fuzzy MLP for classification and rule generation, IEEE Trans. On Neural Networks, 6(1997) 1338–1350

    Article  Google Scholar 

  11. L. M. Fu, A neural-network model for learning domain rules based on its activation function characteristics, IEEE Trans. On Neural Networks, 5(1998) 787–795

    Google Scholar 

  12. J. Vesanto, and E. Alhoniemi, Clustering of the self-organizing map, IEEE Trans. On Neural Networks, Special Issue On Neural Networks for Data Mining and Knowledge Discovery, 3(2000) 586–600

    Article  Google Scholar 

  13. H. Lu, R. Setion and H. Liu, Effective data mining using neural networks, IEEE Trans. On Knowledge and Data Engineering, 6(1996) 957–961

    Google Scholar 

  14. S. Abe, R. Thawonmas and Y. Kobayashi, Feature selection by analyzing class regions approximated by ellipsoids, IEEE Trans. On SMC-Part C: Applications and Reviews, 2(1998) 282–287

    Google Scholar 

  15. W. F. Bloomer, T. S. Dillon and M. Witten, Hybrid BRAINNE: Further developments in extracting symbolic disjunctive rules, Expert Systems with Applications, 3(1997) 163–168

    Article  Google Scholar 

  16. D. H. Wang and T. S. Dillon, Extraction and optimization of classification rules for continuous or mixed mode data using neural nets, Proceedings of SPIE International Conference on Data Mining and Knowledge Discovery: Theory, Tools and Technology III, pp. 38–45, April 16-20, 2001, Orlando, Florida, USA.

    Google Scholar 

  17. H. Yang and T. S. Dillon, Convergence of self-organizing neural algorithm, Neural Networks, 5(1992) 485–493

    Article  Google Scholar 

  18. G. G. Sutton and J. A. Reggia, Effects of normalization constraints on competitive learning, IEEE Trans. On Neural Networks, 3(1994) 502–504

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, VIC, 3083, Australia

    Dianhui Wang & Tharam Dillon

  2. Department of Computer Science and Software Engineering, Newcastle University, Newcastle, Australia

    Elizabeth Chang

Authors
  1. Dianhui Wang
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  2. Tharam Dillon
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  3. Elizabeth Chang
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Editor information

Editors and Affiliations

  1. Centre for Intelligent Systems and Complex Processes, Swinburne University of Technology, John Street, Hawthorn, Victoria, Australia, 3122

    Tim Hendtlass

  2. Department of Computer Science, Southwest Texas State University, 601 University Drive, San Marcos, TX, 78666, USA

    Moonis Ali

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© 2002 Springer-Verlag Berlin Heidelberg

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Wang, D., Dillon, T., Chang, E. (2002). Trading off between Misclassification, Recognition and Generalization in Data Mining with Continuous Features. In: Hendtlass, T., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2002. Lecture Notes in Computer Science(), vol 2358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48035-8_30

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  • DOI: https://doi.org/10.1007/3-540-48035-8_30

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43781-9

  • Online ISBN: 978-3-540-48035-8

  • eBook Packages: Springer Book Archive

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