Evolutionary Induction of Decision Trees for Misclassification Cost Minimization

  • Marek Krȩtowski
  • Marek Grześ
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4431)


In the paper, a new method of decision tree learning for cost-sensitive classification is presented. In contrast to the traditional greedy top-down inducer in the proposed approach optimal trees are searched in a global manner by using an evolutionary algorithm (EA). Specialized genetic operators are applied to modify both the tree structure and tests in non-terminal nodes. A suitably defined fitness function enables the algorithm to minimize the misclassification cost instead of the number of classification errors. The performance of the EA-based method is compared to three well-recognized algorithms on real-life problems with known and randomly generated cost-matrices. Obtained results show that the proposed approach is competitive both in terms of misclassification cost and compactness of the classifier at least for some datasets.


Decision Tree Evolutionary Algorithm Cost Matrix Misclassification Cost Decision Tree Induction 
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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© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Marek Krȩtowski
    • 1
  • Marek Grześ
    • 1
  1. 1.Faculty of Computer Science, Białystok Technical University, Wiejska 45a, 15-351 BiałystokPoland

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