14 citations · 43 across the 10 of their papers we have counts for
10 papers
Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments
Hugo Thimonier, Fabrice Popineau, Arpad Rimmel +2
This study explores the application of anomaly detection (AD) methods in imbalanced learning tasks, focusing on fraud detection using real online credit card payment data. We asses…
Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management
Marc Velay, Bich-Liên Doan, Arpad Rimmel +2
Deep Reinforcement Learning approaches to Online Portfolio Selection have grown in popularity in recent years. The sensitive nature of training Reinforcement Learning agents implie…
Evaluating resampling methods on a real-life highly imbalanced online credit card payments dataset
François de la Bourdonnaye, Fabrice Daniel
Various problems of any credit card fraud detection based on machine learning come from the imbalanced aspect of transaction datasets. Indeed, the number of frauds compared to the…
TracInAD: Measuring Influence for Anomaly Detection
Hugo Thimonier, Fabrice Popineau, Arpad Rimmel +2
As with many other tasks, neural networks prove very effective for anomaly detection purposes. However, very few deep-learning models are suited for detecting anomalies on tabular…
Evaluating categorical encoding methods on a real credit card fraud detection database
François de la Bourdonnaye, Fabrice Daniel
Correctly dealing with categorical data in a supervised learning context is still a major issue. Furthermore, though some machine learning methods embody builtin methods to deal wi…
Bayesian and Dempster-Shafer models for combining multiple sources of evidence in a fraud detection system
Fabrice Daniel
Combining evidence from different sources can be achieved with Bayesian or Dempster-Shafer methods. The first requires an estimate of the priors and likelihoods while the second on…
Financial Time Series Data Processing for Machine Learning
Fabrice Daniel
This article studies the financial time series data processing for machine learning. It introduces the most frequent scaling methods, then compares the resulting stationarity and p…
Seq2Seq and Multi-Task Learning for joint intent and content extraction for domain specific interpreters
Marc Velay, Fabrice Daniel
This study evaluates the performances of an LSTM network for detecting and extracting the intent and content of com- mands for a financial chatbot. It presents two techniques, sequ…
Stock Chart Pattern recognition with Deep Learning
Marc Velay, Fabrice Daniel
This study evaluates the performances of CNN and LSTM for recognizing common charts patterns in a stock historical data. It presents two common patterns, the method used to build t…
Using NLP on news headlines to predict index trends
Marc Velay, Fabrice Daniel
This paper attempts to provide a state of the art in trend prediction using news headlines. We present the research done on predicting DJIA trends using Natural Language Processing…