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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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

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