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On Decision Trees with Minimal Average Depth

  • I. Chikalov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1424)

Abstract

Decision trees are studied in rough set theory [6],[7] and test theory [1], [2], [3] and are used in different areas of applications. The complexity of optimal decision tree (a decision tree with minimal average depth) construction is very high. In the paper some conditions reducing the search are formulated. If these conditions are satisfied, an optimal decision tree for the problem is a result of simple transformation of optimal decision trees for some problems, obtained by decomposition of the initial problem. The decompostion properties are used to show that bounds given in [4] are unimprovable bounds on minimal average depth of decision tree.

Keywords

Decision Tree Average Depth Test Theory Diagnostic Problem Terminal Vertex 
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 1998

Authors and Affiliations

  • I. Chikalov
    • 1
  1. 1.Faculty of Calculating Mathematics and Cybernetics of Nizhni Novgorod State UniversityNizhni NovgorodRussia

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