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Pruning decision trees with misclassification costs

1998; Springer Science+Business Media; Linguagem: Inglês

10.1007/bfb0026682

1611-3349

Jeffrey P. Bradford, Clayton Kunz, Ron Kohavi, Cliff Brunk, Carla E. Brodley,

Machine Learning and Data Classification

We describe an experimental study of pruning methods for decision tree classifiers when the goal is minimizing loss rather than error. In addition to two common methods for error minimization, CART's cost-complexity pruning and C4.5's error-based pruning, we study the extension of cost-complexity pruning to loss and one pruning variant based on the Laplace correction. We perform an empirical comparison of these methods and evaluate them with respect to loss. We found that applying the Laplace correction to estimate the probability distributions at the leaves was beneficial to all pruning methods. Unlike in error minimization, and somewhat surprisingly, performing no pruning led to results that were on par with other methods in terms of the evaluation criteria. The main advantage of pruning was in the reduction of the decision tree size, sometimes by a factor of ten. While no method dominated others on all datasets, even for the same domain different pruning mechanisms are better for different loss matrices.