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On a New Method for Improving Weak Classifiers Using Bayes Metaclassifier

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Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017 (CORES 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 578))

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Abstract

In this paper new algorithm called Bayes metaclassifier (BMC) will be introduced as a method for improving weak classifiers performance. In general, BMC constitutes the probabilistic generalization of any base classifier and has the form of the Bayes scheme. To validate BMC classification two experiments were designed. In the first one three synthetic datasets were generated from normal distribution to calculate and check empirically upper bound for improving base classifier when BMC approach is applied. Furthermore, to validate usefulness of this algorithm extensive simulations from 22 available benchmarks were performed comparing BMC model against 8 base classifiers with different design paradigms.

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References

  1. Alcala-Fdez, J., Fernandez, A., Luengo, J., Derrac, J., Garcia, S., Sanchez, L., Herrera, F.: Keel data-mining software tool: data set repository. J. Multiple-Valued Logic Soft Comput. 17, 255–287 (2011)

    Google Scholar 

  2. Alpaydin, E.: Combined \(5 \,\times \, 2\) cv F test for comparing supervised classification learning algorithms. J. Neural Comput. 11, 1885–1892 (1999)

    Article  Google Scholar 

  3. Asuncion, A., Newman, D.: UCI machine learning repository (2007). http://www.ics.uci.edu/mlearn/MLRepository.html

  4. Duda, R., Hart, P., Stork, D.: Pattern Classification, 3rd edn. Wiley-Interscience, New York (2001)

    MATH  Google Scholar 

  5. Freund, Y.: Boosting a weak learning algorithm by majority. Inf. Comput. 121(2), 256–285 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  6. Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press Professional Inc., San Diego (1990)

    MATH  Google Scholar 

  7. Garcia, S., Fernandez, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf. Sci. 180, 2044–2064 (2010)

    Article  Google Scholar 

  8. Kuncheva, L.: Combining Pattern Classifiers: Methods and Algorithms, 3rd edn. Wiley-Interscience, New York (2004)

    Book  MATH  Google Scholar 

  9. Kuncheva, L.: Real medical data sets repository (2004). http://pages.bangor.ac.uk/~mas00a/activities/real_data.htm

  10. Kurzynski, M., Majak, M.: Meta-Bayes classifier with Markov model applied to the control of bioprosthetic hand. In: Czarnowski, I., Caballero, A.M., Howlett, R.J., Jain, L.C. (eds.) Intelligent Decision Technologies 2016. SIST, vol. 57, pp. 107–117. Springer, Cham (2016). doi:10.1007/978-3-319-39627-9_10

    Chapter  Google Scholar 

  11. Kurzynski, M., Majak, M., Zolnierek, A.: Multiclassifier systems applied to the computer-aided sequential medical diagnosis. J. Biocybern. Biomed. Eng. 36, 619–625 (2016)

    Article  Google Scholar 

  12. Skurichina, M., Duin, R.P.W.: Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal. Appl. 5(2), 121–135 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  13. Skurichina, M., Duin, R.P.W.: Bagging and the random subspace method for redundant feature spaces. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 1–10. Springer, Heidelberg (2001). doi:10.1007/3-540-48219-9_1

    Chapter  Google Scholar 

  14. Strunk Jr., W., White, E.B.: Statistical Decision Theory and Bayesian Analysis, 3rd edn. Springer, New York (1987)

    Google Scholar 

  15. Woloszynski, T.: Classifier competence based on probabilistic modeling (ccprmod.m) (2013)

    Google Scholar 

  16. Woloszynski, T., Kurzynski, M.: A probabilistic model of classifier competence for dynamic ensemble selection. Pattern Recogn. 44, 2656–2668 (2011)

    Article  MATH  Google Scholar 

  17. Woloszynski, T., Kurzynski, M.: A measure of competence based on random classification for dynamic ensemble selection. Inf. Fusion 13, 207–213 (2012)

    Article  MATH  Google Scholar 

  18. Wolpert, D.: Stacked generalization. Neural Netw. 5, 241–259 (1992)

    Article  Google Scholar 

  19. Wozniak, M., Grana, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Inf. Fusion 16, 3–17 (2014)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the statutory funds of the Department of Systems and Computer Networks, Wroclaw University of Science and Technology.

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Authors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Marcin Majak & Marek Kurzyński

Authors
  1. Marcin Majak
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  2. Marek Kurzyński
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Corresponding author

Correspondence to Marcin Majak .

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Editors and Affiliations

  1. Department of Systems and Computer Networks, Wrocław University of Technology, Wrocław, Poland

    Marek Kurzynski

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

    Michal Wozniak

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

    Robert Burduk

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Majak, M., Kurzyński, M. (2018). On a New Method for Improving Weak Classifiers Using Bayes Metaclassifier. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_27

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  • DOI: https://doi.org/10.1007/978-3-319-59162-9_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59161-2

  • Online ISBN: 978-3-319-59162-9

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