Skip to main content

Abstract

The paper discusses the basic problems resulting from the classification of imbalanced data, which are additionally described by a large number of parameters. The paper also presents various optimization methods, including the use of a synthetic indicator that is the product of specificity and the power of sensitivity, which was proposed by the author.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

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

References

  1. Armitage, P.: Metody statystyczne w badaniach medycznych. Państwowy Zakład Wydawnictw Lekarskich, Warszawa (1978)

    Google Scholar 

  2. Bator, M.: Automatyczna detekcja zmian nowotworowych w obrazach mammograficznych z wykorzystaniem dopasowania wzorców i wybranych narzędzi sztucznej inteligencji. Instytut Podstawowych Problemów Techniki PAN. Praca doktorska pod kierunkiem prof. dr hab.inż. Mariusz Jacek Nieniewski (2008)

    Google Scholar 

  3. Chawla, N.: Data mining for imbalanced datasets: an overview. In: Maimon, O. Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, Part 6, pp. 875–886 (2010)

    Chapter  Google Scholar 

  4. Fawcett, T., Provost, F.: Adaptive fraud detection. J. Data Min. Knowl. Disc. 1(3), 291–316 (1997)

    Article  Google Scholar 

  5. Fernández, A., García, S., Herrera, F.: Addressing the classification with imbalanced data: open problems and new challenges on class distribution. In: Hybrid Artificial Intelligent Systems. LNCS, vol. 6678, pp. 1–10 (2011)

    Google Scholar 

  6. García, V., Sánchez, J.S., Mollineda, R.A.: Exploring the performance of resampling strategies for the class imbalance problem. In: Trends in Applied Intelligent Systems. LNCS, vol. 6096, pp. 2010, pp.541–549 (2010)

    Chapter  Google Scholar 

  7. Górecki, H.: Optymalizacja i sterowanie systemów dynamicznych”, Uczelniane Wydawnictwa Naukowo-Dydaktyczne Akademii Górniczo-Hutniczej w Krakowie, Kraków (2006)

    Google Scholar 

  8. Japkowicz, N., Myers, C., Gluck, M.: A novelty detection approach to classification. In: Proceedings of the Fourteenth Joint Conference on Artificial Intelligence, pp. 518–523 (1995)

    Google Scholar 

  9. Kubat, M., Holte, R.C., Matwin, S.: Machine Learning for the detection of oil spills in satellite radar images. J. Mach. Learn. (Special Issue on Applications of Machine Learning and the Knowledge Discovery Process) 30(2–3), 195–215 (1998)

    Article  Google Scholar 

  10. Piotrowska, E: Analysis of morphometric features for computer aided medical diagnosis. PhD thesis, Opole University of Technology (2012)

    Google Scholar 

  11. Stefanowski, J., Wilk, S.: Combining rough sets and rule based classifiers for handling imbalanced data. In: Czaja L. (ed.) Proceedings of Concurrency, Specification and Programming, CS&P 2005 Conference, vol. 2, pp. 497–508 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Piotrowska Ewelina .

Editor information

Editors and Affiliations

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

Ewelina, P. (2020). Evaluation of the Classifiers in Multiparameter and Imbalanced Data Sets. In: Świątek, J., Borzemski, L., Wilimowska, Z. (eds) Information Systems Architecture and Technology: Proceedings of 40th Anniversary International Conference on Information Systems Architecture and Technology – ISAT 2019. ISAT 2019. Advances in Intelligent Systems and Computing, vol 1051. Springer, Cham. https://doi.org/10.1007/978-3-030-30604-5_24

Download citation

Publish with us

Policies and ethics