Skip to main content

Combining One-Class Classifiers for Robust Novelty Detection in Gene Expression Data

  • Conference paper
Advances in Bioinformatics and Computational Biology (BSB 2005)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 3594))

Included in the following conference series:

Abstract

One-class classification techniques are able to, based only on examples of a normal profile, induce a classifier that is capable of identifying novel classes or profile changes. However, the performance of different novelty detection approaches may depend on the domain considered. This paper applies combined one-class classifiers to detect novelty in gene expression data. Results indicate that the robustness of the classification is increased with this combined approach.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Alizadeh, A.A., Eisen, M.B., Davisintegral, R.E., Maintegral, C., Lossos, I.S., Rosenwaldintegral, A., Boldrick, J.C., Sabetintegral, H., Tranintegral, T., Yuintegral, X., Powell, J.I., Yang, L., Marti, G.E., Moore, T., Hudson Jr, J., Lu, L., Lewis, D.B., Tibshirani, R., Sherlock, G., Chan, W.C., Greiner, T.C., Weisenburger, D.D., Armitage, J.O., Warnke, R., Levy, R., Wilson, W., Grever, M.R., Byrd, J.C., Botstein, D., Brown, P.O., Staudt, L.M.: Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)

    Article  Google Scholar 

  2. Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of National Academy of Sciences USA 96, 6745–6750 (1999)

    Article  Google Scholar 

  3. Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)

    Google Scholar 

  4. Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998)

    Google Scholar 

  5. de Souza, B.F.: Seleção de características para svms aplicadas a dados de expressão gênica. Master thesis, Universidade de Sâo Paulo (USP), Instituto de Ciências Matemâticas e de Computação, ICMC (2005)

    Google Scholar 

  6. Duda, R.O., Hart, P.E.: Pattern Classification, 2nd edn. Wiley Interscience, Hoboken (2001)

    MATH  Google Scholar 

  7. Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular classification of cancer: Class discovery and class prediction by gene expression. Science 286, 531–537 (1999)

    Article  Google Scholar 

  8. Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Heidelberg (2001)

    MATH  Google Scholar 

  9. Marsland, S.: Novelty detection in learning systems. Neural Computing Surveys 3, 157–195 (2003)

    Google Scholar 

  10. Parzen, E.: On the estimation of a probability density function and mode. Annals of Mathematical Statistics 33, 1065–1076 (1962)

    Article  MATH  MathSciNet  Google Scholar 

  11. Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Computation 13(7), 1443–1471 (2001)

    Article  MATH  Google Scholar 

  12. Tax, D.M.J.: One-class classifiers. PhD thesis, Delf University of Technology, Faculty of Information Technology and Systems (2001)

    Google Scholar 

  13. Tax, D.M.J.: DDtools, the data description toolbox for matlab. version 1.1.2 (March 2005), http://www-ict.ewi.tudelft.nl/~davidt/dd_tools.html

  14. West, M., Blanchette, C., Dressman, H., Huang, E., Ishida, S., Spang, R., Zuzan, H., Jr., J.A.O., Marks, J.R., Nevins, J.R.: Predicting the clinical status of human breast cancer by using gene expression profiles. Proceedings of National Academy of Sciences USA 98(20), 11462–11467

    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

Spinosa, E.J., de Carvalho, A.C.P.L.F. (2005). Combining One-Class Classifiers for Robust Novelty Detection in Gene Expression Data. In: Setubal, J.C., Verjovski-Almeida, S. (eds) Advances in Bioinformatics and Computational Biology. BSB 2005. Lecture Notes in Computer Science(), vol 3594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11532323_7

Download citation

  • DOI: https://doi.org/10.1007/11532323_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28008-8

  • Online ISBN: 978-3-540-31861-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics