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Combining Linear Classifiers Using Score Function Based on Distance to Decision Boundary

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Artificial Intelligence and Soft Computing (ICAISC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14126))

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Abstract

In this work, we addressed the issue of combining linear classifiers in the geometrical space. In other words, it means that linear classifiers are combined via the combination of their decision hyperplanes. For this purpose, an approach based on the potential functions is proposed. The potential function spans a potential field around each decision plane. The potential fields coming from decision planes are superposed, and the resultant decision field is used as an aggregated model of the ensemble classifier. During the experimental study, the proposed approach was applied to an ensemble built on heterogeneous base classifiers, and it was compared to two reference methods – majority voting and soft voting, respectively. The result shows that the proposed method can improve classification performance metrics compared to soft voting.

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Notes

  1. 1.

    https://github.com/ptrajdos/piecewiseLinearClassifiers/tree/master.

  2. 2.

    https://sci2s.ugr.es/keel/category.php?cat=clas.

References

  1. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996). https://doi.org/10.1007/bf00058655

    Article  MATH  Google Scholar 

  2. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  3. Friedman, M.: A comparison of alternative tests of significance for the problem of \(m\) rankings. Ann. Math. Statist. 11(1), 86–92 (1940). https://doi.org/10.1214/aoms/1177731944

    Article  MathSciNet  MATH  Google Scholar 

  4. Garcia, S., Herrera, F.: An extension on “statistical comparisons of classifiers over multiple data sets’’ for all pairwise comparisons. J. Mach. Learn. Res. 9, 2677–2694 (2008)

    MATH  Google Scholar 

  5. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software. SIGKDD Explor. Newsl. 11(1), 10 (2009). https://doi.org/10.1145/1656274.1656278

    Article  Google Scholar 

  6. Hall, M.A.: Correlation-based feature selection for machine learning. Ph.D. thesis, The University of Waikato (1999)

    Google Scholar 

  7. Holm, S.: A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6(2), 65–70 (1979). https://doi.org/10.2307/4615733

    Article  MathSciNet  MATH  Google Scholar 

  8. Hüllermeier, E., Fürnkranz, J.: On predictive accuracy and risk minimization in pairwise label ranking. J. Comput. Syst. Sci. 76(1), 49–62 (2010). https://doi.org/10.1016/j.jcss.2009.05.005

    Article  MathSciNet  MATH  Google Scholar 

  9. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms, 1 edn. Wiley-Interscience (2004)

    Google Scholar 

  10. Luaces, O., Díez, J., Barranquero, J., del Coz, J.J., Bahamonde, A.: Binary relevance efficacy for multilabel classification. Prog. Artif. Intell. 1(4), 303–313 (2012). https://doi.org/10.1007/s13748-012-0030-x

    Article  Google Scholar 

  11. Pearson, K.: LIII. on lines and planes of closest fit to systems of points in space. London Edinburgh Dublin Philos. Mag. J. Sci. 2(11), 559–572 (1901). https://doi.org/10.1080/14786440109462720

  12. Platt, J., et al.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classif. 10(3), 61–74 (1999)

    Google Scholar 

  13. Rokach, L.: Pattern classification using ensemble methods, vol. 75. World Scientific (2010)

    Google Scholar 

  14. Skurichina, M., Duin, R.P.: Bagging for linear classifiers. Pattern Recognit. 31(7), 909–930 (1998). https://doi.org/10.1016/s0031-3203(97)00110-6

    Article  Google Scholar 

  15. Wilcoxon, F.: Individual comparisons by ranking methods. Biom. Bull. 1(6), 80 (1945). https://doi.org/10.2307/3001968

    Article  Google Scholar 

  16. Yekutieli, D., Benjamini, Y.: The control of the false discovery rate in multiple testing under dependency. Ann. Statist. 29(4), 1165–1188 (2001). https://doi.org/10.1214/aos/1013699998

    Article  MathSciNet  MATH  Google Scholar 

  17. Zadrozny, B., Elkan, C.: Learning and making decisions when costs and probabilities are both unknown. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2001). https://doi.org/10.1145/502512.502540

  18. Zadrozny, B., Elkan, C.: Transforming classifier scores into accurate multiclass probability estimates. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2002. ACM Press (2002). https://doi.org/10.1145/775047.775151

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Author information

Authors and Affiliations

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

    Pawel Trajdos, Robert Burduk & Andrzej Kasprzak

Authors
  1. Pawel Trajdos
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  2. Robert Burduk
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  3. Andrzej Kasprzak
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Corresponding author

Correspondence to Robert Burduk .

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Krakow, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Trajdos, P., Burduk, R., Kasprzak, A. (2023). Combining Linear Classifiers Using Score Function Based on Distance to Decision Boundary. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2023. Lecture Notes in Computer Science(), vol 14126. Springer, Cham. https://doi.org/10.1007/978-3-031-42508-0_36

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  • DOI: https://doi.org/10.1007/978-3-031-42508-0_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42507-3

  • Online ISBN: 978-3-031-42508-0

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