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Published on May 30, 2015
Classification, in the context of machine learning, deals with the problem of predicting the class of a set of examples given their features. Traditionally, classification methods aim at minimizing the misclassification of examples, in which an example is misclassified if the predicted class is different from the true class. Such a traditional framework assumes that all misclassification errors carry the same cost. This is not the case in many real-world applications such as credit card fraud detection, credit scoring, churn modeling and direct marketing. In this talk I would like to present CostCla a cost-sensitive classification library. The library incorporates several cost-sensitive algorithms. Moreover, during the talk I will show the huge differences in profit when using traditional machine learning algorithms versus cost-sensitive algorithms, on several real-world databases.