Advertisement

Attribute Number Reduction Process and Nearest Neighbor Methods in Machine Learning

  • Aleksander Sokołowski
  • Anna Gładysz
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 35)

Abstract

Several nearest neighbor methods were applied to process of decision making to E522144 and modified bases, which are the collections of cases of melanocytic skin lesions. Modification of the bases consists in reducing the number of base attributes from 14 to 13, 4, 3, 2 and finally 1. The reduction process consists in concatenations of values of particular attributes. The influence of this process on the quality of decision making process is reported in the paper.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    1. R. J. Friedman, D. S. Rigel, A. W. Kopf, Early detection of malignant melanoma: the role of physician examination and self-examination of the skin, CA Cancer J. Clin., 35 (1985) 130–151.CrossRefGoogle Scholar
  2. 2.
    2. J. W. Stolz, O. Braun-Falco, P. Bilek, A. B. Landthaler, A. B. Cogneta, Color Atlas of Dermatology, Blackwell Science Inc., Cambridge, MA (1993).Google Scholar
  3. 3.
    3. J. W. Grzymala-Busse, LERS A system for learning from examples based on rough sets, in Intelligent Decision Support. Handbook of Application and Advances of the Rough Sets Theory. R. Slowinski (ed.), Kluwer Academic Publishers, Dordrecht, Boston, London (1992) 3–18.Google Scholar
  4. 4.
    4. J. W. Grzymala-Busse, A new version of the rule induction system LERS, Fundamenta Informaticae 31 (1997) 27–39.zbMATHGoogle Scholar
  5. 5.
    5. A. Alvarez, F.M. Brown, J. W. Grzymala-Busse, and Z. S. Hippe, Optimization of the ABCD formula used for melanoma diagnosis, Proc. of the II PWM2003, Int. Conf. On Intelligent Information Processing and WEB Mining Systems, Zakopane, Poland, June 2–5 (2003) 233–240.Google Scholar
  6. 6.
    6. J. P. Grzymala-Busse, J. W. Grzymala-Busse, and Z. S. Hippe, Melanoma prediction using data mining system LERS, Proceedings of the 25th Anniversary Annual International Computer Software and Applications Conference COMPSAC 2001, Chicago, IL, October 8–12 (2001) 615–620.Google Scholar
  7. 7.
    7. J. W. Grzymala-Busse, and Z. S. Hippe, Postprocessing of rule sets induced from a melanoma data sets, Proc. of the COMPSAC 2002, 26th Annual International Conference on Computer Software and Applications, Oxford, England, August 26–29 (2002) 1146–1151.Google Scholar
  8. 8.
    8. J. W. Grzymala-Busse, and Z. S. Hippe, A search for the best data mining method to predict melanoma, Proceedings of the RSCTC 2002, Third International Conference on Rough Sets and Current Trends In Computing, Malvern, PA, October 14–16 (2002) Springer-Verlag, 538–545.Google Scholar
  9. 9.
    9. R. Rohwer and M. Morciniec, A theoretical and experimental account of n-tuple classifier performance, Neural Computation 8 (1996) 657–670.Google Scholar
  10. 10.
    10. W. Duch, Neural distance methods, Proc. 3-rd Conf. on Neural Networks and Their Applications, Kule, Poland, Oct. 14–18 (1997).Google Scholar
  11. 11.
    11. J. H. Friedman, Flexible metric nearest neighbor classification, Technical Report, Dept. of Statistics, Stanford University (1994).Google Scholar
  12. 12.
    12. R. Tadeusiewicz, M. Flasiński, Image recognition, PWN, Warszawa, (1991) (in polish).Google Scholar
  13. 13.
    13. P. R. Krishnaiah, L. N. Kanal (eds), Handbook of statistics 2: classification, pattern recognition and reduction of dimensionality, North Holland, Amsterdam, (1982).Google Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • Aleksander Sokołowski
    • 1
  • Anna Gładysz
    • 1
  1. 1.Rzeszow University of TechnologyRzeszówPoland

Personalised recommendations