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Improving the Performance of Fuzzy Rule Based Classification Systems for Highly Imbalanced Data-Sets Using an Evolutionary Adaptive Inference System

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Bio-Inspired Systems: Computational and Ambient Intelligence (IWANN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5517))

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Abstract

In this contribution, we study the influence of an Evolutionary Adaptive Inference System with parametric conjunction operators for Fuzzy Rule Based Classification Systems. Specifically, we work in the context of highly imbalanced data-sets, which is a common scenario in real applications, since the number of examples that represents one of the classes of the data-set (usually the concept of interest) is usually much lower than that of the other classes.

Our experimental study shows empirically that the use of the parametric conjunction operators enables simple Fuzzy Rule Based Classification Systems to enhance their performance for data-sets with a high imbalance ratio.

Supported by the Spanish Ministry of Science and Technology under Project TIN2008-06681-C06-01.

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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. 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 

  3. 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 

  4. Crockett, K.A., Bandar, Z., Mclean, D., O’Shea, J.: On constructing a fuzzy inference framework using crisp decision trees. Fuzzy Sets and Systems 157, 2809–2832 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  5. Alcalá-Fdez, J., Herrera, F., Márquez, F.A., Peregrín, A.: Increasing fuzzy rules cooperation based on evolutionary adaptive inference systems. International Journal of Intelligent Systems 22(9), 1035–1064 (2007)

    Article  MATH  Google Scholar 

  6. Márquez, F.A., Peregrín, A., Herrera, F.: Cooperative evolutionary learning of fuzzy rules and parametric aggregation connectors for mamdani linguistic fuzzy systems. IEEE Transactions on Fuzzy Systems 15(6), 1162–1178 (2007)

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  9. Su, C.T., Hsiao, Y.H.: An evaluation of the robustness of MTS for imbalanced data. IEEE Transactions on Knowledge Data Engineering 19(10), 1321–1332 (2007)

    Article  Google Scholar 

  10. 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 

  11. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  12. García, S., Fernández, A., Luengo, J., Herrera, F.: A study of statistical techniques and performance measures for genetics-based machine learning: Accuracy and interpretability. Soft Computing 13(10), 959–977 (2009)

    Article  Google Scholar 

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

    MATH  Google Scholar 

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

    MATH  Google Scholar 

  15. 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 

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Author information

Authors and Affiliations

  1. Dept. of Computer Science and A.I., University of Granada, Spain

    Alberto Fernández & Francisco Herrera

  2. Dept. of Computer Science, University of Jaén, Spain

    María José del Jesus

Authors
  1. Alberto Fernández
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  2. María José del Jesus
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  3. Francisco Herrera
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Editor information

Editors and Affiliations

  1. Departamento de Ingeniería Electrónica, Universitat Politècnica de Catalunya (UPC). E.T.S.I. de Telecomunicación., , , ,, Campus Norte, Edificio C4, C/ Jordi Girona, 1-3, E08034, Barcelona, Spain

    Joan Cabestany

  2. Grupo ISIS, Dpto. Tecnología Electrónica ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Francisco Sandoval

  3. Department of Computer Architecture and Computer Technology, University of Granada, Spain

    Alberto Prieto

  4. Department of Informatics, University of Salamanca, Salamanca, Spain

    Juan M. Corchado

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© 2009 Springer-Verlag Berlin Heidelberg

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Fernández, A., del Jesus, M.J., Herrera, F. (2009). Improving the Performance of Fuzzy Rule Based Classification Systems for Highly Imbalanced Data-Sets Using an Evolutionary Adaptive Inference System. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_37

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  • DOI: https://doi.org/10.1007/978-3-642-02478-8_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02477-1

  • Online ISBN: 978-3-642-02478-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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