A New Cost Sensitive Decision Tree Method : Application for Mammograms Classification

摘要

Make a decision has often many results and repercussions. These results do not have the same importance according to the considered phenomenon. This situation can be translated by the introduction of the cost concept in the learning process. In this article, we propose a method able to take into account 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.