Class-Oriented Reduction of Decision Tree Complexity

  • José-Luis Polo
  • Fernando Berzal
  • Juan-Carlos Cubero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4994)


In some classification problems, apart from a good model, we might be interested in obtaining succinct explanations for particular classes. Our goal is to provide simpler classification models for these classes without a significant accuracy loss. In this paper, we propose some modifications to the splitting criteria and the pruning heuristics used by standard top-down decision tree induction algorithms. This modifications allow us to take each particular class importance into account and lead us to simpler models for the most important classes while, at the same time, the overall classifier accuracy is preserved.


Class Weight Tree Pruning Pruning Strategy Split Criterion Accuracy Loss 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • José-Luis Polo
    • 1
  • Fernando Berzal
    • 1
  • Juan-Carlos Cubero
    • 1
  1. 1.Department of Computer Sciences and Artificial IntelligenceUniversity of Granada.GranadaSpain

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