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A Wrapper Evolutionary Approach for Supervised Multivariate Discretization: A Case Study on Decision Trees

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Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 403))

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Abstract

The main objective of discretization is to transform numerical attributes into discrete ones. The intention is to provide the possibility to use some learning algorithms which require discrete data as input and to help the experts to understand the data more easily. Due to the fact that in classification problems there are high interactions among multiple attributes, we propose the use of evolutionary algorithms to select a subset of cut points for multivariate discretization based on a wrapper fitness function. The algorithm proposed has been compared with the best state-of-the-art discretizers with two decision trees-based classifiers: C4.5 and PUBLIC. The results reported indicate that our proposal outperforms the rest of the discretizers in terms of accuracy and requiring a lower number of intervals.

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Notes

  1. 1.

    They are specified in Table 1.

References

  1. Alcalá-Fdez, J., Sánchez, L., García, S., del Jesus, M.J., Ventura, S., Garrell, J.M., Otero, J., Romero, C., Bacardit, J., Rivas, V.M., Fernández, J.C., Herrera, F.: KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput. 13(3), 307–318 (2009)

    Article  Google Scholar 

  2. Bache, K., Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml

  3. Cios, K.J., Pedrycz, W., Swiniarski, R.W., Kurgan, L.A.: Data Mining: A Knowledge Discovery Approach. Springer, New York (2007)

    MATH  Google Scholar 

  4. Dash, M., Liu, H.: Consistency-based search in feature selection. Artif. Intell. 151(1–2), 155–176 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  5. Elomaa, T., Rousu, J.: General and efficient multisplitting of numerical attributes. Mach. Learn. 36, 201–244 (1999)

    Article  MATH  Google Scholar 

  6. Eshelman, L.J.: The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination. In: FOGA, pp. 265–283 (1990)

    Google Scholar 

  7. Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence (IJCAI), pp. 1022–1029 (1993)

    Google Scholar 

  8. Freitas, A.A.: Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer, New York (2002)

    Book  MATH  Google Scholar 

  9. García, S., Luengo, J., Sáez, J.A., López, V., Herrera, F.: A survey of discretization techniques: taxonomy and empirical analysis in supervised learning. IEEE Trans. Knowl. Data Eng. 25(4), 734–750 (2013)

    Article  Google Scholar 

  10. García, S., Luengo, J., Herrera, F.: Data Preprocessing in Data Mining. Springer, New York (2015)

    Book  Google Scholar 

  11. He, Z., Tian, S., Huang, H.: EMVD-BDC: an evolutionary multivariate discretization approach for association rules. J. Comput. Inf. Syst. 2(4), 1343–1350 (2006)

    Google Scholar 

  12. Kerber, R.: ChiMerge: discretization of numeric attributes. In: National Conference on Artificial Intelligence American Association for Artificial Intelligence (AAAI92), pp. 123–128 (1992)

    Google Scholar 

  13. Kurgan, L.A., Cios, K.J.: CAIM discretization algorithm. IEEE Trans. Knowl. Data Eng. 16(2), 145–153 (2004)

    Article  Google Scholar 

  14. Liu, H., Hussain, F., Tan, C.L., Dash, M.: Discretization: an enabling technique. Data Min. Knowl. Discov. 6(4), 393–423 (2002)

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  16. Sheng, W., Liu, X., Fairhurst, M.C.: A niching memetic algorithm for simultaneous clustering and feature selection. IEEE Trans. Knowl. Data Eng. 20(7), 868–879 (2008)

    Article  Google Scholar 

  17. Tay, F.E.H., Shen, L.: A modified Chi2 algorithm for discretization. IEEE Trans. Knowl. Data Eng. 14, 666–670 (2002)

    Article  Google Scholar 

  18. Wu, X., Kumar, V. (eds.): The Top Ten Algorithms in Data Mining. Chapman & Hall/CRC Data Mining and Knowledge Discovery, Boca Raton (2009)

    Google Scholar 

  19. Yang, Y., Webb, G.I.: Discretization for Naive-Bayes learning: managing discretization bias and variance. Mach. Learn. 74(1), 39–74 (2009)

    Article  Google Scholar 

  20. Zighed, D.A., Rabaséda, S., Rakotomalala, R.: FUSINTER: a method for discretization of continuous attributes. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 6, 307–326 (1998)

    Article  MATH  Google Scholar 

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Acknowledgments

This work was partially supported by the Spanish Ministry of Science and Technology under project TIN2011-28488 and the Andalusian Research Plans P11-TIC-7765, P10-TIC-6858.

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Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    Sergio Ramírez-Gallego, Salvador García, José Manuel Benítez & Francisco Herrera

Authors
  1. Sergio Ramírez-Gallego
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  2. Salvador García
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  3. José Manuel Benítez
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  4. Francisco Herrera
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Corresponding author

Correspondence to Sergio Ramírez-Gallego .

Editor information

Editors and Affiliations

  1. Department of Systems, Wrocław University of Technology, Wroclaw, Poland

    Robert Burduk

  2. Department of Systems and Computer, Wrocław University of Technology, Wroclaw, Poland

    Konrad Jackowski

  3. Department of Systems and Computer, Wrocław University of Technology, Wroclaw, Poland

    Marek Kurzyński

  4. Dept. of Systems and Computer Networks, Wrocław University of Technology, Wroclaw, Poland

    Michał Woźniak

  5. Department of Systems, Wrocław University of Technology, Wroclaw, Poland

    Andrzej Żołnierek

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Ramírez-Gallego, S., García, S., Benítez, J.M., Herrera, F. (2016). A Wrapper Evolutionary Approach for Supervised Multivariate Discretization: A Case Study on Decision Trees. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-26227-7_5

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-26227-7

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