Fraud Detection by Stacking Cost-Sensitive Decision Trees
Recently, we published a research paper showing how it is possible to detect fraudulent credit card transactions with a high level of accuracy and a low number of false positives. By using ensembles of cost-sensitive decision trees, we can save up to 73 percent of losses stemming from fraud. Here’s how.
Classification, in the context of machine learning, deals with the problem of accurately sorting examples in a dataset into sub-groups or classes. Traditionally, classification methods aim to minimize the misclassification of examples, where the predicted class of is different from the true class. Such a traditional framework assumes that all misclassification errors carry the same cost. However, this is not the case in many real-world applications: Methods that take into account variations in misclassification costs are known as cost-sensitive classifiers. [Read More]