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  • The Inria's Research Teams produce an annual Activity Report presenting their activities and their results of the year. These reports include the team members, the scientific program, the software developed by the team and the new results of the year. The report also describes the grants, contracts and the activities of dissemination and teaching. Finally, the report gives the list of publications of the year.

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Section: New Results

Tree-based Cost-Sensitive Methods for Fraud Detection in Imbalanced Data

Participants: G. Metzler, X. Badiche, B. Belkasmi, E. Fromont, A. Habrard, M. Sebban

Bank fraud detection is a difficult classification problem where the number of frauds is much smaller than the number of genuine transactions. The authors of [20] present cost sensitive tree-based learning strategies applied in this context of highly imbalanced data. The paper first proposes a cost sensitive splitting criterion for decision trees that takes into account the cost of each transaction. Then the criterion is extended with a decision rule for classification with tree ensembles. The authors then propose a new cost-sensitive loss for gradient boosting. Both methods have been shown to be particularly relevant in the context of imbalanced data. Experiments on a proprietary dataset of bank fraud detection in retail transactions show that the presented cost sensitive algorithms increase the retailer's benefits by 1,43% compared to non cost-sensitive ones and that the gradient boosting approach outperforms all its competitors.