Olivier Caelen

Orcid: 0000-0001-6970-9825

According to our database1, Olivier Caelen authored at least 37 papers between 2005 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
An Adversary Model of Fraudsters' Behavior to Improve Oversampling in Credit Card Fraud Detection.
IEEE Access, 2023

Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives.
Proceedings of the Second ACM Data Economy Workshop, 2023

2021
Combining unsupervised and supervised learning in credit card fraud detection.
Inf. Sci., 2021

2020
Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs.
Future Gener. Comput. Syst., 2020

Managing a pool of rules for credit card fraud detection by a Game Theory based approach.
Future Gener. Comput. Syst., 2020

2019
Batch and incremental dynamic factor machine learning for multivariate and multi-step-ahead forecasting.
Int. J. Data Sci. Anal., 2019

Multiple perspectives HMM-based feature engineering for credit card fraud detection.
Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 2019

Understanding Telecom Customer Churn with Machine Learning: From Prediction to Causal Inference.
Proceedings of the Artificial Intelligence and Machine Learning, 2019

2018
Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy.
IEEE Trans. Neural Networks Learn. Syst., 2018

SCARFF: A scalable framework for streaming credit card fraud detection with spark.
Inf. Fusion, 2018

Correction to: Streaming active learning strategies for real-life credit detection: assessment and visualization.
Int. J. Data Sci. Anal., 2018

Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization.
Int. J. Data Sci. Anal., 2018

Sequence classification for credit-card fraud detection.
Expert Syst. Appl., 2018

A Multivariate and Multi-step Ahead Machine Learning Approach to Traditional and Cryptocurrencies Volatility Forecasting.
Proceedings of the ECML PKDD 2018 Workshops, 2018

Online Non-linear Gradient Boosting in Multi-latent Spaces.
Proceedings of the Advances in Intelligent Data Analysis XVII, 2018

Embeddings of Categorical Variables for Sequential Data in Fraud Context.
Proceedings of the International Conference on Advanced Machine Learning Technologies and Applications, 2018

2017
A Bayesian interpretation of the confusion matrix.
Ann. Math. Artif. Intell., 2017

Injecting Semantic Background Knowledge into Neural Networks using Graph Embeddings.
Proceedings of the 26th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises, 2017

Machine Learning for Multi-step Ahead Forecasting of Volatility Proxies.
Proceedings of the Second Workshop on MIning DAta for financial applicationS (MIDAS 2017) co-located with the 2017 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2017), 2017

Efficient Top Rank Optimization with Gradient Boosting for Supervised Anomaly Detection.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Improving Card Fraud Detection Through Suspicious Pattern Discovery.
Proceedings of the Advances in Artificial Intelligence: From Theory to Practice, 2017

An Assessment of Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection.
Proceedings of the 2017 IEEE International Conference on Data Science and Advanced Analytics, 2017

2016
A graph-based, semi-supervised, credit card fraud detection system.
Proceedings of the Complex Networks & Their Applications V - Proceedings of the 5th International Workshop on Complex Networks and their Applications (COMPLEX NETWORKS 2016), Milan, Italy, November 30, 2016

2015
APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions.
Decis. Support Syst., 2015

Calibrating Probability with Undersampling for Unbalanced Classification.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2015

When is Undersampling Effective in Unbalanced Classification Tasks?
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Credit card fraud detection and concept-drift adaptation with delayed supervised information.
Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

2014
Learned lessons in credit card fraud detection from a practitioner perspective.
Expert Syst. Appl., 2014

Using HDDT to avoid instances propagation in unbalanced and evolving data streams.
Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

2013
Racing for Unbalanced Methods Selection.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2013, 2013

2011
A Selecting-the-Best Method for Budgeted Model Selection.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

2010
A dynamic programming strategy to balance exploration and exploitation in the bandit problem.
Ann. Math. Artif. Intell., 2010

2007
Improving the Exploration Strategy in Bandit Algorithms.
Proceedings of the Learning and Intelligent Optimization, Second International Conference, 2007

Machine Learning Techniques for Decision Support in Anesthesia.
Proceedings of the Artificial Intelligence in Medicine, 2007

2006
Machine Learning Techniques to Enable Closed-Loop Control in Anesthesia.
Proceedings of the 19th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2006), 2006

2005
Speeding up Feature Selection by Using an Information Theoretic Bound.
Proceedings of the BNAIC 2005, 2005

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.
Proceedings of the BNAIC 2005, 2005


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