Nothing Special   »   [go: up one dir, main page]

Skip to main content

Selective Gaussian Naïve Bayes Model for Diffuse Large-B-Cell Lymphoma Classification: Some Improvements in Preprocessing and Variable Elimination

  • Conference paper
Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3571))

Abstract

In this work, we present some significant improvements for for feature selection in wrapper methods. They are two: the first of them consists in a proper preordering of the feature set; and the second one consists in the application of an irrelevant feature elimination method, where the irrelevance condition is subjected to the partial selected feature subset by the wrapper method. We validate these approaches with the Diffuse Large B-Cell Lymphoma subtype classification problem and we show that these two changes are an important improvement in the computation cost and the classification accuracy of these wrapper methods in this domain.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Hand, D.: Discrimination and Classification. John Wiley, Chichester (1981)

    MATH  Google Scholar 

  2. Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using bayesian networks to analyze expression data. Journal of Computational Biology 7, 601–620 (2000)

    Article  Google Scholar 

  3. Inza, I., Sierra, B., Blanco, R., Larrañaga, P.: Gene selection by sequential wrapper approaches in microarray cancer class prediction. Journal of Intelligent and Fuzzy Systems 12, 25–34 (2002)

    MATH  Google Scholar 

  4. Langley, P., Iba, W., Thompson, K.: An analysis of bayesian classifiers. In: National Conference on Artificial Intelligence, pp. 223–228 (1992)

    Google Scholar 

  5. Domingos, P., Pazzani, M.J.: On the optimality of the simple bayesian classifier under zero-one loss. Machine Learning 29, 103–130 (1997)

    Article  MATH  Google Scholar 

  6. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131–163 (1997)

    Article  MATH  Google Scholar 

  7. Langley, P., Sage, S.: Induction of selective bayesian classifiers. In: Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, pp. 399–406 (1994)

    Google Scholar 

  8. John, G.H., Kohavi, R.: Irrelevant features and the subset selection problem. In: Proceedings of the Eleventh International Conference on Machine Learning, pp. 121–129 (1994)

    Google Scholar 

  9. Golub, T.R., et al.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)

    Article  Google Scholar 

  10. Inza, I., Larrañaga, P., Blanco, R., Cerrolaza, A.: Filter versus wrapper gene selection approaches in dna microarray domains. Artificial Intelligence in Medicine, special issue in Data mining in genomics and proteomics 31(2), 91–103 (2004)

    Google Scholar 

  11. Hsu, C.N., Huang, H.J., Wong, T.T.: Why discretization works for naïve bayesian classifiers. In: Proc. 17th International Conf. on Machine Learning, pp. 399–406. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  12. John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann, San Francisco (1995)

    Google Scholar 

  13. Cowell, R., Dawid, A., Lauritzen, S., Spiegelhalter, D.: Probabilistic Networks and Expert Systems. In: Statistics for Engineering and Information Science, Springer, New York (1999)

    Google Scholar 

  14. Wright, G., Tan, B., Rosenwald, A., Hurt, E.H., Wiestner, A., Staudt, L.M.: A gene expression-based method to diagnose clinically distinct subgroups of diffuse large b cell lymphoma. Proceedings of National Academy of Sciences of the United States of America 100, 9991–9996 (2003)

    Article  Google Scholar 

  15. Cano, A., Castellano, F.G., Masegosa, A., Moral, S.: Application of a selective gaussian naïve bayes model for diffuse large-b-cell lymphoma classification. In: Proceedings of the Second European Workshop in Probabilistic Graphicals Models, Leiden, Holland, pp. 33–40 (2004)

    Google Scholar 

  16. Kittler, J.: Feature set search algorithms. In: Chen, C.H. (ed.) Pattern Recognition and Signal Processing, Sijthoff & Noordhoff, pp. 41–60 (1978)

    Google Scholar 

  17. Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. John Wiley, New York (1973)

    MATH  Google Scholar 

  18. Stone, M.: An asymptotic equivalence of choice of model by cross-validation and akaike’s criterion. Journal of the Real Statistical Society 38, 38–47 (1997)

    Google Scholar 

  19. Aha, D.W., Bankert, R.L.: Feature selection for case-based classification of cloud types: An empirical comparision. In: Working Notes of the AAAI-94 Workshop on Case-Based Reasoning, pp. 106–112. AAAI Press, Seattle (1994)

    Google Scholar 

  20. Langley, P., Sage, S.: Oblivious decision trees and abstract cases. In: Working Notes of the AAAI 1994 Workshop on Case-Based Reasoning, AAAI Press, Seattle (1994)

    Google Scholar 

  21. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97, 273–324 (1997)

    Article  MATH  Google Scholar 

  22. Allmuallim, H., Dietterich, T.: Learning with many irrelevant features. In: Ninth National Conference on Artificial Intelligence, pp. 547–552. MIT Press, Cambridge (1991)

    Google Scholar 

  23. Alizadeh, A., et al.: Distinct types of diffuse large B–cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)

    Article  Google Scholar 

  24. Zhang, H., Yu, C.Y., Singer, B.: Cell and tumor classification using gene expression data: Construction of forests. Proceedings of the National Academy of Sciences 100, 4168–4172 (2003)

    Article  Google Scholar 

  25. Li, L., Weinberg, C.R., Darden, T.A., Pedersen, L.G.: Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the ga/knn method. Bioinformatics 17, 1131–1142 (2001)

    Article  Google Scholar 

  26. Ando, T., Katayama, M., Seto, M., Kobayashi, T., Honda, H.: Selection of causal gene sets from transciptional profiling by fnn modeling an prediction of lymphoma outcome. Gene Informatics 13, 278–279 (2002)

    Google Scholar 

  27. Rosenwald, A., Wright, G., Chan, W.C., Connors, J.M., Campo, E., Fisher, R.I., Gascoyne, R.D., Muller-Hermelink, H.K., Smealand, E.B., Staudt, L.M.: The use of molecular profiling to predict survival after chemotherapy for diffuse large-b-cell lymphoma. New England Journal of Medicine 346, 1937–1947 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Cano, A., Castellano, J.G., Masegosa, A.R., Moral, S. (2005). Selective Gaussian Naïve Bayes Model for Diffuse Large-B-Cell Lymphoma Classification: Some Improvements in Preprocessing and Variable Elimination. In: Godo, L. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2005. Lecture Notes in Computer Science(), vol 3571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11518655_76

Download citation

  • DOI: https://doi.org/10.1007/11518655_76

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27326-4

  • Online ISBN: 978-3-540-31888-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics