Advertisement

Improving the Accuracy of the KNN Method When Using an Even Number K of Neighbors

  • Alberto Palacios PawlovskyEmail author
  • Daisuke Kurematsu
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 64)

Abstract

The kNN (k Nearest Neighbors) method is a classification method that could show low accuracy figures for even values of k. This paper details one method to improve the accuracy of the kNN method for those cases. It also shows one method that could improve the accuracy of it for biased classification sets and for odd values of k.

References

  1. 1.
    P-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Pearson Education Inc., 2006.Google Scholar
  2. 2.
    Shelly Gupta, Dharminder Kumar and Anand Sharma, “ Data Mining Classification Techniques Applied for Cancer Breast Diagnosis and Prognosis,” Indian Journal of Computer Science and Engineering, Vol. 2, No. 2, pp. 188–195, May 2011.Google Scholar
  3. 3.
    Shweta Kharya, “ Using Data Mining Techniques for Diagnosis and Prognosis of Cancer Disease,” International Journal of Computer Science and Information Technology, Vol. 2, No. 2, pp. 55–66, April 2012.CrossRefGoogle Scholar
  4. 4.
    Gouda I. Salama, M. B. Abdelhalim, and Magdy Abd-elghany Zeid, “Breast Cancer Diagnosis on Three Different Data sets Using Multi-Classifiers,” International Journal of Computer and Information Technology Vol. 1, Issue 1, pp. 36–43, September 2012.Google Scholar
  5. 5.
    Shomona G. Jacob and R. Geetha Ramani, “Efficient Classifier for Classification of Prognosis Breast Cancer Data Through Data Mining Techniques,” Proceedings of the World Congress on Engineering and Computer Science 2012, Vol. I, October 2012.Google Scholar
  6. 6.
    Ethem Alpaydin, Introduction to Machine Learning 2nd. Edition, The MIT Press, 2010. Chap. 8.Google Scholar
  7. 7.
    Manish Sarkar and Tze-Yun Leong, “Application of k-Nearest Neighbors Algorithm on Breast Cancer Diagnosis Problem,” Proceedings of American Medical Informatics Association (AMIA) Annual Symposium, pp. 759–763, 2000.Google Scholar
  8. 8.
    Jini R. Marsilin and G. Wiselin Jiji, “An Efficient CBIR Approach for Diagnosing the Stages of Breast Cancer Using KNN Classifier,” Bonfring International Journal of Advances in Image Processing, Vol. 2, No. 1, pp. 1–5, March 2012.Google Scholar
  9. 9.
    Alberto Palacios Pawlovsky and Mai Nagahashi, “A Method to Select a Good Setting for the kNN Algorithm when Using it for Breast Cancer Prognosis,” Proceedings of the 2nd. IEEE International Conference on Biomedical and Health Informatics (BHI 2014), pp. 189–192, Sevilla, Spain, June, 2014.Google Scholar
  10. 10.
    Katsuyoshi Odajima and Alberto Palacios Pawlovsky, “A Detailed Description of the Use of the kNN Method for Breast Cancer Diagnosis,” Proceedings of the 7th International Conference on Biomedical Engineering and Informatics (BMEI 2014) Dalian, pp. 606–610, China, October 2014.Google Scholar
  11. 11.
  12. 12.
    Seyyid A. Medjahed, Tamazouzt A. Saadi, and Abdelkader Benyettou, “Breast Cancer Diagnosis by using k-Nearest Neighbor with Different Distances and Classification Rules,” International Journal of Computer Applications, Vol. 62, No.1, pp. 1–5, January 2013.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Alberto Palacios Pawlovsky
    • 1
    Email author
  • Daisuke Kurematsu
    • 2
  1. 1.Faculty of Biomedical Engineering, Department of Clinical EngineeringToin University of YokohamaKanagawaJapan
  2. 2.Department of Clinical EngineeringToin University of YokohamaKanagawaJapan

Personalised recommendations