Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives

Data economy relies on data-driven systems and complex machine learning applications are fueled by them. Unfortunately, however, machine learning models are exposed to fraudulent activities and adversarial attacks, which threaten their security and trustworthiness. In the last decade or so, the research interest on adversarial machine learning has grown significantly, revealing how learning applications could be severely impacted by effective attacks. Although early results of adversarial machine learning indicate the huge potential of the approach to specific domains such as image processing, still there is a gap in both the research literature and practice regarding how to generalize adversarial techniques in other domains and applications. Fraud detection is a critical defense mechanism for data economy, as it is for other applications as well, which poses several challenges for machine learning. In this work, we describe how attacks against fraud detection systems differ from other applications of adversarial machine learning, and propose a number of interesting directions to bridge this gap

Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization

Credit card fraud detection is a very challenging problem because of the specific nature of transaction data and the labeling process. The transaction data is peculiar because they are obtained in a streaming fashion, they are strongly imbalanced and prone to non-stationarity. The labeling is the outcome of an active learning process, as every day human investigators contact only a small number of cardholders (associated to the riskiest transactions) and obtain the class (fraud or genuine) of the related transactions. An adequate selection of the set of cardholders is therefore crucial for an efficient fraud detection process. In this paper, we present a number of active learning strategies and we investigate their fraud detection accuracies. We compare different criteria (supervised, semi-supervised and unsupervised) to query unlabeled transactions. Finally, we highlight the existence of an exploitation/exploration trade-off for active learning in the context of fraud detection, which has so far been overlooked in the literature

Sélection séquentielle en environnement aléatoire appliquée à l'apprentissage supervisé

Cette thèse se penche sur les problèmes de décisions devant être prises de manière séquentielle au sein d'un environnement aléatoire. Lors de chaque étape d'un tel problème décisionnel, une alternative doit être sélectionnée parmi un ensemble d'alternatives. Chaque alternative possède un gain moyen qui lui est propre et lorsque l'une d'elles est sélectionnée, celle-ci engendre un gain aléatoire. La sélection opérée peut suivre deux types d'objectifs.Dans un premier cas, les tests viseront à maximiser la somme des gains collectés. Un juste compromis doit alors être trouvé entre l'exploitation et l'exploration. Ce problème est couramment dénommé dans la littérature scientifique "multi-armed bandit problem".Dans un second cas, un nombre de sélections maximal est imposé et l'objectif consistera à répartir ces sélections de façon à augmenter les chances de trouver l'alternative présentant le gain moyen le plus élevé. Ce deuxième problème est couramment repris dans la littérature scientifique sous l'appellation "selecting the best".La sélection de type gloutonne joue un rôle important dans la résolution de ces problèmes de décision et opère en choisissant l'alternative qui s'est jusqu'ici montrée optimale. Or, la nature généralement aléatoire de l'environnement rend incertains les résultats d'une telle sélection. Dans cette thèse, nous introduisons une nouvelle quantité, appelée le "gain espéré d'une action gloutonne". Sur base de quelques propriétés de cette quantité, de nouveaux algorithmes permettant de résoudre les deux problèmes décisionnels précités seront proposés.Une attention particulière sera ici prêtée à l'application des techniques présentées au domaine de la sélection de modèles en l'apprentissage artificiel supervisé. La collaboration avec le service d'anesthésie de l'Hôpital Erasme nous a permis d'appliquer les algorithmes proposés à des données réelles, provenant du milieu médical. Nous avons également développé un système d'aide à la décision dont un prototype a déjà été testé en conditions réelles sur un échantillon restreint de patients.Doctorat en Sciencesinfo:eu-repo/semantics/nonPublishe

A dynamic programming strategy to balance exploration and exploitation in the bandit problem

The K-armed bandit problem is a well-known formalization of the exploration versus exploitation dilemma. In this learning problem, a player is confronted to a gambling machine with K arms where each arm is associated to an unknown gain distribution. The goal of the player is to maximize the sum of the rewards. Several approaches have been proposed in literature to deal with the K-armed bandit problem. This paper introduces first the concept of "expected reward of greedy actions" which is based on the notion of probability of correct selection (PCS), well-known in simulation literature. This concept is then used in an original semi-uniform algorithm which relies on the dynamic programming framework and on estimation techniques to optimally balance exploration and exploitation. Experiments with a set of simulated and realistic bandit problems show that the new DP-greedy algorithm is competitive with state-of-the-art semi-uniform techniques. © 2010 Springer Science+Business Media B.V.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

How to allocate a restricted budget of leave-one-out assessments for effective model selection in machine learning: A comparison of state-of-art techniques

The problem of selecting the best among several alternatives in a stochastic context has been the object of researcli in several domains: stochastic optimization, discrete-event stochastic simulation, experimental design. A particular instance of this problem is of particular relevance in machine learning where the search of the model which could best represent a finite set of data asks for comparing several alternatives on the basis of a finite set of noisy data. This paper aims to bridge a gap between these different communities by comparing experimentally the effectiveness of techniques proposed in the simulation and in the stochastic dynamic programming community in performing a model selection task. In particular, we will consider here a model selection task in regression where the alternatives are represented by a finite set of K-nearest neighbors models with different values of the structural parameter K. The techniques we compare are i) a two-stage selection technique proposed in the stochastic simulation community, ii) a stochastic dynamic programming approach conceived to address the multi-armed bandit problem, iii) a racing method, iv) a greedy approach, v) a round-search technique.SCOPUS: cp.pinfo:eu-repo/semantics/publishe

How to allocate a restricted budget of leave-one-out assessments for effective model selection in machine learning: a comparison of state-of-the-art techniques

BNAIC '05info:eu-repo/semantics/publishe

On the evolution of the expected gain of a greedy action in the bandit problem

Rapport consultable à l'adresse suivante :http://www.ulb.ac.be/di/map/ocaelen/mugEvolution.pdfTechnical report nr 589info:eu-repo/semantics/publishe

Machine learning techniques for decision support in anesthesia

SCOPUS: cp.pinfo:eu-repo/semantics/publishe