Cost-Sensitive Learning with Neural Networks.

A few different approaches for cost-sensitive modifications of the back- propagation learning algorithm for multilayered feedforw ard neural networks, which are thoroughly tested and evaluated on several standard benchmark domains.

Abstract

In the usual setting of Machine Learning, classifiers are typically evaluated by estimating their error rate (or equivalently, the classification accuracy) on the test data. However, this makes sense only if all errors have equal (uniform) costs. When the costs of errors differ between each other, the classifiers should be evaluated by comparing the total costs of the errors.