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CAIP
2009
Springer

Decision Trees Using the Minimum Entropy-of-Error Principle

13 years 7 months ago
Decision Trees Using the Minimum Entropy-of-Error Principle
Binary decision trees based on univariate splits have traditionally employed so-called impurity functions as a means of searching for the best node splits. Such functions use estimates of the class distributions. In the present paper we introduce a new concept to binary tree design: instead of working with the class distributions of the data we work directly with the distribution of the errors originated by the node splits. Concretely, we search for the best splits using a minimum entropy-of-error (MEE) strategy. This strategy has recently been applied in other areas (e.g. regression, clustering, blind source separation, neural network training) with success. We show that MEE trees are capable of producing good results with often simpler trees, have interesting generalization properties and in the many experiments we have performed they could be used without pruning.
Joaquim Marques de Sá, João Gama, Ra
Added 02 Sep 2010
Updated 02 Sep 2010
Type Conference
Year 2009
Where CAIP
Authors Joaquim Marques de Sá, João Gama, Raquel Sebastião, Luís A. Alexandre
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