C4.5: programs for machine learning
C4.5: programs for machine learning
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Pruning Decision Trees with Misclassification Costs
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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Making a decision has often many results and repercussions. These results don't have the same importance according to the considered phenomenon. This situation can be described by the introduction of the cost concept in the learning process. In this article, we propose a method able to integrate the costs in the automatic learning process. We focus our work on the misclassification cost and we use decision trees as a supervised learning technique. Promising results are obtained using the proposed method.