Cost-Sensitive Learning with Neural Networks. 论文

摘要

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.