Skip to main content
SpringerLink
Log in

Employing Decision Templates to Imbalanced Data Classification

  • Conference paper
  • First Online:
Hybrid Artificial Intelligent Systems (HAIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12344))

Included in the following conference series:

Abstract

Data difficulties as imbalanced class distribution cause that the methods which can produce reliable predictive models remain a focus of intense research. This work attempts employing the concept of Decision Templates for the mentioned classification task. Additionally, a modification to the original method is introduced, which uses many decision templates for each class instead of one per class. The usefulness of the algorithms employing the idea of Decision Template algorithm is evaluated based on extensive experimental study and backed-up with a thorough statistical analysis. We also present an in-depth discussion of both the positive and negative impacts of the proposed approach.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Notes

  1. 1.

    https://github.com/w4k2/decision-templates.

References

  1. Alcalá-Fdez, J., et al.: Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Multiple-Valued Log. Soft Comput. 17, 255–287 (2011)

    Google Scholar 

  2. Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: Optics: ordering points to identify the clustering structure. In: Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, pp. 49–60. Association for Computing Machinery (1999)

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  4. Dietrich, C., Palm, G., Schwenker, F.: Decision templates for the classification of bioacoustic time series. Inf. Fusion 4(2), 101–109 (2003). https://doi.org/10.1016/S1566-2535(03)00017-4, http://www.sciencedirect.com/science/article/pii/S1566253503000174

  5. Dietterich, T.G.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10(7), 1895–1923 (1998)

    Article  Google Scholar 

  6. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, USA (2000)

    MATH  Google Scholar 

  7. GarcíÂa, V., Sánchez, J., Mollineda, R.: On the effectiveness of preprocessing methods when dealing with different levels of class imbalance. Knowl.-Based Syst. 25(1), 13–21 (2012). special Issue on New Trends in Data Mining

    Google Scholar 

  8. Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)

    Article  Google Scholar 

  9. Krawczyk, B.: Learning from imbalanced data: open challenges and future directions. Prog. Artif. Intell. 5(4), 221–232 (2016). https://doi.org/10.1007/s13748-016-0094-0

    Article  Google Scholar 

  10. Krawczyk, B., Wozniak, M., Schaefer, G.: Cost-sensitive decision tree ensembles for effective imbalanced classification. Appl. Soft Comput. 14, 554–562 (2014). https://doi.org/10.1016/j.asoc.2013.08.014

  11. Ksieniewicz, P.: Undersampled majority class ensemble for highly imbalanced binary classification. In: Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, vol. 94, pp. 82–94. PMLR (2018)

    Google Scholar 

  12. Kuncheva, L.I., Bezdek, J.C., Duin, R.P.: Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recogn. 34(2), 299–314 (2001)

    Article  Google Scholar 

  13. Liu, X., Wu, J., Zhou, Z.: Exploratory undersampling for class-imbalance learning. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 39(2), 539–550 (2009)

    Article  Google Scholar 

  14. Min, J.-K., Cho, S.-B.: Multiple classifier fusion using k-nearest localized templates. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds.) IDEAL 2007. LNCS, vol. 4881, pp. 447–456. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-77226-2_46

    Chapter  Google Scholar 

  15. Nguyen, T.T., et al.: Evolving an optimal decision template for combining classifiers. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) ICONIP 2019. LNCS, vol. 11953, pp. 608–620. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36708-4_50

    Chapter  Google Scholar 

  16. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  17. Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bull. 1(6), 80–83 (1945)

    Article  Google Scholar 

  18. Woźniak, M., Graña, 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 Polish National Science Centre under the grant No. 2017/27/B/ST6/01325 as well as by the statutory funds of the Department of Systems and Computer Networks,Wroclaw University of Science and Technology.

Author information

Authors and Affiliations

  1. Department of Systems and Computer Networks, Faculty of Electronics, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Szymon Wojciechowski & Michał Woźniak

Authors
  1. Szymon Wojciechowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michał Woźniak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Szymon Wojciechowski .

Editor information

Editors and Affiliations

  1. University of Oviedo, Oviedo, Spain

    Enrique Antonio de la Cal

  2. University of Oviedo, Oviedo, Spain

    José Ramón Villar Flecha

  3. University of A Coruña, Ferrol, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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

Wojciechowski, S., Woźniak, M. (2020). Employing Decision Templates to Imbalanced Data Classification. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-61705-9_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61704-2

  • Online ISBN: 978-3-030-61705-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation