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ICANNGA
2007
Springer

Evolutionary Induction of Decision Trees for Misclassification Cost Minimization

10 years 6 hour ago
Evolutionary Induction of Decision Trees for Misclassification Cost Minimization
Abstract. In the paper, a new method of decision tree learning for costsensitive 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.
Marek Kretowski, Marek Grzes
Added 16 Aug 2010
Updated 16 Aug 2010
Type Conference
Year 2007
Where ICANNGA
Authors Marek Kretowski, Marek Grzes
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