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Improved Support Vector Machine Generalization Using Normalized Input Space

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AI 2006: Advances in Artificial Intelligence (AI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4304))

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Abstract

Data pre-processing always plays a key role in learning algorithm performance. In this research we consider data pre-processing by normalization for Support Vector Machines (SVMs). We examine the normalization affect across 112 classification problems with SVM using the rbf kernel. We observe a significant classification improvement due to normalization. Finally we suggest a rule based method to find when normalization is necessary for a specific classification problem. The best normalization method is also automatically selected by SVM itself.

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References

  1. Vapnik, V.: The Nature of The Statistical Learning Theory. Springer, New York (1995)

    MATH  Google Scholar 

  2. Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons, Chichester (1998)

    MATH  Google Scholar 

  3. Vapnik, V.N.: An Overview of Statistical Learning Theory. IEEE Transaction on Neural Networks 10(5), 988–999 (1999)

    Article  Google Scholar 

  4. Graf, A., Borer, S.: Normalization in Support Vector Machines. In: Proc. DAGM Pattern Recognition, Springer, Berlin (2001)

    Google Scholar 

  5. Pontil, M., Verri, A.: Support Vector Machines for 3-D Object Recognition. IEEE Trans. Pattern Anal. Machine Intell. 20, 637–646 (1998)

    Article  Google Scholar 

  6. Graf, A.B.A., Smola, A.J., Borer, S.: Classification in a Normalized Feature Space Using Support Vector Machines. IEEE Transactions on Neural Networks 14(3), 597–605 (2003)

    Article  Google Scholar 

  7. Herbrich, R., Graepel, T.: A PAC-bayesian margin bound for linear classifiers: Why SVM’s work. Advances in Neural Information Processing Systems 13 (2001)

    Google Scholar 

  8. Ali, S., Smith, K.A.: Kernel Width Selection for SVM Classification-A Meta-Learning Approach. International Journal of Data Warehousing and Mining, Idea Publishers, USA, 78–97 (2005)

    Google Scholar 

  9. Blake, C., Merz, C.J.: UCI Repository of Machine Learning Databases. University of California, Irvine, CA (2002), http://www.ics.uci.edu/~mlearn/MLRepository.html

  10. Lim, T.-S.: Knowledge Discovery Central, Datasets (2002), http://www.KDCentral.com/

  11. Kennedy, R.L., Lee, Y., Roy, B.V., Reed, C.D., Lippman, R.P.: Solving Data Mining Problems Through Pattern Recognition. Prentice-Hall, Englewood Cliffs (1997)

    Google Scholar 

  12. Statistics toolbox user’s guide, Version 3, The MathWorks, Inc. USA (2001)

    Google Scholar 

  13. Smith, K.A., Woo, F., Ciesielski, V., Ibrahim, R.: Modelling The Relationship Between Problem Characteristics and Data Mining Algorithm Performance Using Neural Networks. In: Dagli, C., et al. (eds.) Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Data Mining, and Complex Systems, vol. 11, pp. 357–362. ASME Press (2001)

    Google Scholar 

  14. Smith, K.A., Woo, F., Ciesielski, V., Ibrahim, R.: Matching Data Mining Algorithm Suitability to Data Characteristics Using a Self-Organising Map. In: Abraham, A., Koppen, M. (eds.) Hybrid Information Systems, pp. 169–180. Physica-Verlag, Heidelberg (2002)

    Google Scholar 

  15. Mandenhall, W., Sincich, T.: Statistics for Engineering and The Sciences, 4th edn. Prentice-Hall, Englewood Cliffs (1995)

    Google Scholar 

  16. Tamhane, A.C., Dunlop, D.D.: Statistics and Data Analysis. Prentice Hall, Englewood Cliffs (2000)

    Google Scholar 

  17. Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufman Publishers, San Mateo (1993)

    Google Scholar 

  18. Duin, R.P.W.: A note on comparing classifier. Pattern Recognition Letters 1, 529–536 (1996)

    Article  Google Scholar 

  19. Witten, I.H., Frank, E.: Data Mining: practical machine learning tool and technique with Java implementation. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  20. Evans, M., Hastings, N., Peacock, B.: Statistical Distributions, 2nd edn. John Wiley and Sons, Chichester (1993)

    MATH  Google Scholar 

  21. Johnson, N., Kotz, S.: Distributions in Statistics: Continuous Univariate Distributions, 2nd edn. John Wiley and Sons, Chichester (1970)

    MATH  Google Scholar 

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Ali, S., Smith-Miles, K.A. (2006). Improved Support Vector Machine Generalization Using Normalized Input Space. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_40

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  • DOI: https://doi.org/10.1007/11941439_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49787-5

  • Online ISBN: 978-3-540-49788-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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