Skip to main content
SpringerLink
Log in

A Genetic Algorithm for Feature Selection and Granularity Learning in Fuzzy Rule-Based Classification Systems for Highly Imbalanced Data-Sets

  • Conference paper
Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods (IPMU 2010)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 80))

Abstract

This contribution proposes a Genetic Algorithm for jointly performing a feature selection and granularity learning for Fuzzy Rule-Based Classification Systems in the scenario of data-sets with a high imbalance degree. We refer to imbalanced data-sets when the class distribution is not uniform, a situation that it is present in many real application areas. The aim of this work is to get more compact and precise models by selecting the adequate variables and adapting the number of fuzzy labels for each problem.

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

Access this chapter

Log in via an institution
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.

References

  1. Chawla, N.V., Japkowicz, N., Kolcz, A.: Editorial: special issue on learning from imbalanced data sets. SIGKDD Explorations 6(1), 1–6 (2004)

    Article  Google Scholar 

  2. Ishibuchi, H., Nakashima, T., Nii, M.: Classification and modeling with linguistic information granules: Advanced approaches to linguistic Data Mining. Springer, Heidelberg (2004)

    MATH  Google Scholar 

  3. Fernández, A., García, S., Del Jesus, M.J., Herrera, F.: A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets. Fuzzy Sets and Systems 159(18), 2378–2398 (2008)

    Article  MathSciNet  Google Scholar 

  4. Villar, P., Fernández, A., Herrera, F.: A Genetic Learning of the Fuzzy Rule-Based Classification System Granularity for highly Imbalanced Data-Sets. In: 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2009), pp. 1689–1694 (2009)

    Google Scholar 

  5. Chi, Z., Yan, H., Pham, T.: Fuzzy algorithms with applications to image processing and pattern recognition. World Scientific, Singapore (1996)

    Google Scholar 

  6. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)

    Google Scholar 

  7. Batista, G.E.A.P.A., Prati, R.C., Monard, M.C.: A Study of the Behaviour of Several Methods for Balancing Machine Learning Training Data. SIGKDD Explorations 6(1), 20–29 (2004)

    Article  Google Scholar 

  8. Asuncion, A., Newman, D.J.: UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences, http://www.ics.uci.edu/~mlearn/MLRepository.html

  9. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligent Research 16, 321–357 (2002)

    MATH  Google Scholar 

  10. García, S., Herrera, F.: An Extension on “Statistical Comparisons of Classifiers over Multiple data sets” for all Pairwise Comparisons. Journal of Machine Learning Research 9, 2607–2624 (2008)

    MATH  Google Scholar 

  11. Orriols-Puig, A., Bernadó-Mansilla, E.: Evolutionary rule-based systems for imbalanced datasets. Soft Computing 13(3), 213–225 (2009)

    Article  Google Scholar 

  12. Weiss, G.M.: Mining with rarity: a unifying framework. SIGKDD Explorations 6(1), 7–19 (2004)

    Article  Google Scholar 

  13. Huang, J., Ling, C.X.: Using AUC and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17(3), 299–310 (2005)

    Article  Google Scholar 

  14. Ishibuchi, H., Yamamoto, T.: Rule Weight Specification in Fuzzy Rule-Based Classification Systems. IEEE Transactions on Fuzzy Systems 13, 428–435 (2005)

    Article  Google Scholar 

  15. Sheskin, D.: Handbook of parametric and nonparametric statistical procedures, 2nd edn. Chapman & Hall/CRC, Boca Raton (2006)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Software Engineering, Spain

    Pedro Villar

  2. Department of Computer Science and Artificial Intelligence, E.T.S. Ing. Informática y de Telecomunicación, University of Granada, Spain

    Alberto Fernández & Francisco Herrera

Authors
  1. Pedro Villar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alberto Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco Herrera
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Fachbereich Mathematik und Informatik, Philipps-Universität Marburg, Marburg, Germany

    Eyke Hüllermeier

  2. Department of Knowledge Processing and Language Engineering, Otto-von-Guericke University of Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany

    Rudolf Kruse

  3. Fakultät für Elektrotechnik und Informationstechnik, Technische Universität Dortmund, 44221, Dortmund, Germany

    Frank Hoffmann

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Villar, P., Fernández, A., Herrera, F. (2010). A Genetic Algorithm for Feature Selection and Granularity Learning in Fuzzy Rule-Based Classification Systems for Highly Imbalanced Data-Sets. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods. IPMU 2010. Communications in Computer and Information Science, vol 80. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14055-6_78

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-14055-6_78

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14054-9

  • Online ISBN: 978-3-642-14055-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation