Post Supervised Based Learning of Feature Weight Values

  • Vassilis S. Moustakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3955)


The article presents in detail a model for the assessment of feature weight values in context of inductive machine learning. Weight assessment is done based on learned knowledge and can not be used to assess feature values prior to learning. The model is based on Ackoff’s theory of behavioral communication. The model is also used to assess rule value importance. We present model heuristics and present a simple application based on the “play” vs. “not play” golf application. Implications about decision making modeling are discussed.


Rule Formation Feature Weight Training Instance Minority Class Knowledge Gain 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Carter, C., Catlett, J.: Assessing Credit Card Applications Using Machine Learning. IEEE Expert, 71–79 (Fall, 1987)Google Scholar
  2. 2.
    Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)Google Scholar
  3. 3.
    Ackoff, R.L.: Towards a behavioral model of communication. Management Science 4, 218–234 (1958)CrossRefGoogle Scholar
  4. 4.
    Kononenko, I., Bratko, I.: Information based evaluation criterion for classifiers’ performance. Machine Learning 6(1), 67–80 (1991)Google Scholar
  5. 5.
    Gaga, E., Moustakis, V., Vlachakis, Y., Charissis, G.: ID+: Enhancing Medical Knowledge Acquisition Using Inductive Machine Learning. Applied Artificial Intelligence: An International Journal 10(2), 79–94 (1996)CrossRefGoogle Scholar
  6. 6.
    Kahneman, D., Tversky, A.: Intutive prediction: biases and corrective procedures, TIMS. Studies in the Management Sciences 12, 313–327 (1979)Google Scholar
  7. 7.
    Starr, M.K., Zeleny, M.: MCDM: State and future of the arts. In: Starr, M.K., Zeleny, M. (eds.) TIMS Studies in Management Sciences 6: Multiple Criteria Decision Making, pp. 5–29. North-Holland, Amsterdam (1977)Google Scholar
  8. 8.
    Fujarewicz, K., Weinch, M.: Selecting differentially expressed genes for colon tumor classification. Int. J. Appl. Math. Comput. Sci. 13(3), 327–335 (2003)MathSciNetMATHGoogle Scholar
  9. 9.
    Maclin, R., Shavlik, J.W.: Using knowledge based neural networks to improve algorithms: refining the Chou-Fasman algorithm for protein folding. Machine Learning 11(2-3), 195–215 (1993)CrossRefGoogle Scholar
  10. 10.
    Doumpos, M., Zopounidis, C.: Multicriteria Decision Aid Classification Methods. Kluwer Academic Publishers, Dordrecht (2002)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Vassilis S. Moustakis
    • 1
    • 2
  1. 1.Institute of Computer ScienceFoundation for Research and Technology-Hellas (FORTH)Heraklion, CreteGreece
  2. 2.Department of Production and Management EngineeringTechnical University of CreteKounoupidiana, Chania, CreteGreece

Personalised recommendations