Fraud detection is an important research area where machine learning has a significant role to play. An important task in that context, on which the quality of the results obtained depends, is feature engineering. Unfortunately, this is very time and human consuming. Thus, in this article, we present the DuSVAE model that consists of a generative model that takes into account the sequential nature of the data. It combines two variational autoencoders that can generate a condensed representation of the input sequential data that can then be processed by a classifier to label each new sequence as fraudulent or genuine. The experiments we carried out on a large real-word dataset, from the Worldline company, demonstrate the ability of our system to better detect frauds in credit card transactions without any feature engineering effort.

We address the problem of domain adaptation for binary classification which arises when the distributions generating the source learning data and target test data are somewhat different. We consider the challenging case where no target labeled data is available. From a theoretical standpoint, a classifier has better generalization guarantees when the two domain marginal distributions are close. We study a new direction based on a recent framework of Balcan et al. allowing to learn linear classifiers in an explicit projection space based on similarity functions that may be not symmetric and not positive semi-definite. We propose a general method for learning a good classifier on target data with generalization guarantees and we improve its efficiency thanks to an iterative procedure by reweighting the similarity function - compatible with Balcan et al. framework - to move closer the two distributions in a new projection space. Hyper parameters and reweighting quality are controlled by a reverse validation procedure. Our approach is based on a linear programming formulation and shows good adaptation performances with very sparse models. We evaluate it on a synthetic problem and on real image annotation task.