Determining attribute relevance in decision trees

  • Mirsad Hadzikadic
  • Ben F. Bohren
Communications Session 6B Learning and Discovery Systems
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1325)


Concept formation, an artificial intelligence classification technique, has been used successfully by many researchers in predicting outcomes of new objects based on a decision tree built from previously seen objects. All systems based on concept formation are capable of providing outcome predictions. INC2.5, a concept formation system, goes further by (a) implementing an algorithm that identifies relevant attributes and (b) administering a test that measures system's predictive ability based on the reduced attribute set. These capabilities are important to users attempting to prove a specific feature's contribution to an outcome. This paper focuses on the algorithm for analyzing attribute relevance as opposed to the classification and prediction techniques that have been explained in previous publications.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Mirsad Hadzikadic
    • 1
  • Ben F. Bohren
    • 2
  1. 1.Carolinas HealthCare System and University of North Carolina at CharlotteUSA
  2. 2.Speech Systems Inc.USA

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