Skip to main content
SpringerLink
Log in

Randomized Reference Classifier with Gaussian Distribution and Soft Confusion Matrix Applied to the Improving Weak Classifiers

  • Conference paper
  • First Online:
Progress in Computer Recognition Systems (CORES 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 977))

Included in the following conference series:

Abstract

In this paper, an issue of building the RRC model using probability distributions other than beta distribution is addressed. More precisely, in this paper, we propose to build the RRR model using the truncated normal distribution. Heuristic procedures for expected value and the variance of the truncated-normal distribution are also proposed. The proposed approach is tested using SCM-based model for testing the consequences of applying the truncated normal distribution in the RRC model. The experimental evaluation is performed using four different base classifiers and seven quality measures. The results showed that the proposed approach is comparable to the RRC model built using beta distribution. What is more, for some base classifiers, the truncated-normal-based SCM algorithm turned out to be better at discovering objects coming from minority classes.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

Similar content being viewed by others

Notes

  1. 1.

    https://github.com/ptrajdos/rrcBasedClassifiers/tree/develop.

  2. 2.

    https://github.com/ptrajdos/MLResults/blob/master/data/slDataFull.zip.

  3. 3.

    https://github.com/ptrajdos/MLResults/tree/master/RandomizedClassifiers/RRC_NormalDistribution_CORES2019.

References

  1. Berger JO (1985) Statistical decision theory and Bayesian analysis. Springer, New York. https://doi.org/10.1007/978-1-4757-4286-2

    Book  Google Scholar 

  2. Bergmann B, Hommel G (1988) Improvements of general multiple test procedures for redundant systems of hypotheses. In: Multiple hypothesenprüfung/multiple hypotheses testing. Springer, Heidelberg, pp 100–115.

    Chapter  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  4. Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inform Theory 13(1):21–27. https://doi.org/10.1109/tit.1967.1053964

    Article  MATH  Google Scholar 

  5. David HA, Nagaraja HN (2003) Order Statistics. Wiley, Hoboken. https://doi.org/10.1002/0471722162

    Book  MATH  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  7. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139. https://doi.org/10.1006/jcss.1997.1504

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  12. Hand DJ, Yu K (2001) Idiot’s bayes: not so stupid after all? Int Stat Rev/Revue Internationale de Statistique 69(3):385. https://doi.org/10.2307/1403452

    Article  MATH  Google Scholar 

  13. Johnson N (1994) Continuous Univariate Distributions. Wiley, New York

    MATH  Google Scholar 

  14. Kuncheva L, Bezdek J (1998) Nearest prototype classification: clustering, genetic algorithms, or random search? IEEE Trans Syst Man Cybern Part C (Appl Rev) 28(1):160–164. https://doi.org/10.1109/5326.661099

    Article  Google Scholar 

  15. Kurzynski M, Majak M (2016) Meta-bayes classifier with Markov model applied to the control of bioprosthetic hand. In: Intelligent decision technologies 2016: proceedings of the 8th KES international conference on intelligent decision technologies (KES-IDT 2016) – part II. Springer, Cham, pp 107–117

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  17. Lysiak R, Kurzynski M, Woloszynski T (2014) Optimal selection of ensemble classifiers using measures of competence and diversity of base classifiers. Neurocomputing 126:29–35. https://doi.org/10.1016/j.neucom.2013.01.052

    Article  Google Scholar 

  18. Majak M, Kurzynski M (2018) On a new method for improving weak classifiers using bayes metaclassifier. In: Proceedings of the 10th international conference on computer recognition systems, CORES 2017. Springer, Cham, pp 258–267.

    Google Scholar 

  19. Provost F, Domingos P (2003) Tree induction for probability-based ranking. Mach Learn 52(3):199–215. https://doi.org/10.1023/a:1024099825458

    Article  MATH  Google Scholar 

  20. Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco

    Google Scholar 

  21. Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45(4). https://doi.org/10.1016/j.ipm.2009.03.002

    Article  Google Scholar 

  22. Trajdos P, Kurzynski M (2016) A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier. Int J Appl Math Comput Sci 26(1). https://doi.org/10.1515/amcs-2016-0012

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  24. Woloszynski T, Kurzynski M (2009) On a new measure of classifier competence applied to the design of multiclassifier systems. In: International conference on image analysis and processing. Springer, pp 995–1004

    Google Scholar 

  25. Woloszynski T, Kurzynski M (2011) A probabilistic model of classifier competence for dynamic ensemble selection. Pattern Recogn 44(10–11):2656–2668. https://doi.org/10.1016/j.patcog.2011.03.020

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  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.

Author information

Authors and Affiliations

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

    Pawel Trajdos & Marek Kurzynski

Authors
  1. Pawel Trajdos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marek Kurzynski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pawel Trajdos .

Editor information

Editors and Affiliations

  1. Faculty of Electronics, Wroclaw University of Science and Technology, Wrocław, Poland

    Robert Burduk

  2. Faculty of Electronics, Wroclaw University of Science and Technology, Wrocław, Poland

    Marek Kurzynski

  3. Faculty of Electronics, Wroclaw University of Science and Technology, Wrocław, Poland

    Michał Wozniak

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Trajdos, P., Kurzynski, M. (2020). Randomized Reference Classifier with Gaussian Distribution and Soft Confusion Matrix Applied to the Improving Weak Classifiers. In: Burduk, R., Kurzynski, M., Wozniak, M. (eds) Progress in Computer Recognition Systems. CORES 2019. Advances in Intelligent Systems and Computing, vol 977. Springer, Cham. https://doi.org/10.1007/978-3-030-19738-4_33

Download citation

Publish with us

Policies and ethics

Search

Navigation