Olivier Caelen

4.3k total citations · 4 hit papers
32 papers, 2.3k citations indexed

About

Olivier Caelen is a scholar working on Artificial Intelligence, Management Science and Operations Research and Accounting. According to data from OpenAlex, Olivier Caelen has authored 32 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 7 papers in Management Science and Operations Research and 4 papers in Accounting. Recurrent topics in Olivier Caelen's work include Imbalanced Data Classification Techniques (18 papers), Machine Learning and Data Classification (7 papers) and Machine Learning and Algorithms (5 papers). Olivier Caelen is often cited by papers focused on Imbalanced Data Classification Techniques (18 papers), Machine Learning and Data Classification (7 papers) and Machine Learning and Algorithms (5 papers). Olivier Caelen collaborates with scholars based in Belgium, France and United States. Olivier Caelen's co-authors include Gianluca Bontempi, Andrea Dal Pozzolo, Yann‐Aël Le Borgne, Fabrizio Carcillo, R. A. Johnson, Giacomo Boracchi, Cesare Alippi, Michael Granitzer, Liyun He-Guelton and Sylvie Calabretto and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Information Sciences.

In The Last Decade

Olivier Caelen

29 papers receiving 2.2k citations

Hit Papers

4 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy

2017 338 citations Andrea Dal Pozzolo, Giacomo Boracchi et al. IEEE Transactions on Neural Networks and Learning Systems profile →
Calibrating Probability with Undersampling for Unbalanced Classification

2015 320 citations Andrea Dal Pozzolo, Olivier Caelen et al. profile →
Learned lessons in credit card fraud detection from a practitioner perspective

2014 294 citations Andrea Dal Pozzolo, Olivier Caelen et al. Expert Systems with Applications profile →
Sequence classification for credit-card fraud detection

2018 268 citations Johannes Jurgovsky, Michael Granitzer et al. Expert Systems with Applications profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Olivier Caelen Belgium	15	2.0k	704	475	311	299	32	2.3k
Andrea Dal Pozzolo Belgium	7	1.1k 0.6×	369 0.5×	227 0.5×	146 0.5×	180 0.6×	9	1.2k
Vicente García Mexico	23	1.4k 0.7×	638 0.9×	278 0.6×	47 0.2×	322 1.1×	62	2.2k
Yann‐Aël Le Borgne Belgium	12	787 0.4×	285 0.4×	177 0.4×	123 0.4×	192 0.6×	26	1.1k
Kyung‐shik Shin South Korea	15	1.1k 0.5×	734 1.0×	185 0.4×	22 0.1×	262 0.9×	29	2.2k
Chris Drummond Canada	11	946 0.5×	88 0.1×	334 0.7×	25 0.1×	148 0.5×	24	1.4k
Qinbao Song China	20	1.1k 0.6×	48 0.1×	320 0.7×	51 0.2×	143 0.5×	53	1.7k
Daniel Sánchez Spain	21	1.0k 0.5×	61 0.1×	775 1.6×	78 0.3×	45 0.2×	123	1.8k
Julio López Chile	17	708 0.4×	125 0.2×	101 0.2×	31 0.1×	94 0.3×	54	1.2k
Sara del Río Spain	11	864 0.4×	73 0.1×	316 0.7×	23 0.1×	170 0.6×	13	1.1k
Serdar Korukoğlu Türkiye	16	1.1k 0.5×	43 0.1×	318 0.7×	31 0.1×	109 0.4×	43	2.0k

Countries citing papers authored by Olivier Caelen

Since Specialization

Citations

This map shows the geographic impact of Olivier Caelen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Olivier Caelen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Olivier Caelen more than expected).

Fields of papers citing papers by Olivier Caelen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Olivier Caelen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Olivier Caelen. The network helps show where Olivier Caelen may publish in the future.

Co-authorship network of co-authors of Olivier Caelen

This figure shows the co-authorship network connecting the top 25 collaborators of Olivier Caelen. A scholar is included among the top collaborators of Olivier Caelen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Olivier Caelen. Olivier Caelen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Caelen, Olivier, et al.. (2024). Calibration methods in imbalanced binary classification. Annals of Mathematics and Artificial Intelligence. 92(5). 1319–1352. 2 indexed citations

Caelen, Olivier, et al.. (2024). Machine Learning Methods for Credit Card Fraud Detection: A Survey. IEEE Access. 12. 158939–158965. 2 indexed citations

Caelen, Olivier, et al.. (2023). An Adversary Model of Fraudsters’ Behavior to Improve Oversampling in Credit Card Fraud Detection. IEEE Access. 11. 136666–136679. 5 indexed citations

Portier, Pierre-Édouard, et al.. (2019). Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs. Future Generation Computer Systems. 102. 393–402. 94 indexed citations

Borgne, Yann‐Aël Le, et al.. (2018). Batch and incremental dynamic factor machine learning for multivariate and multi-step-ahead forecasting. International Journal of Data Science and Analytics. 7(4). 311–329. 13 indexed citations

Jurgovsky, Johannes, Michael Granitzer, Sylvie Calabretto, et al.. (2018). Sequence classification for credit-card fraud detection. Expert Systems with Applications. 100. 234–245. 268 indexed citations breakdown →

Carcillo, Fabrizio, Yann‐Aël Le Borgne, Olivier Caelen, & Gianluca Bontempi. (2018). Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization. International Journal of Data Science and Analytics. 5(4). 285–300. 75 indexed citations

Pozzolo, Andrea Dal, Giacomo Boracchi, Olivier Caelen, Cesare Alippi, & Gianluca Bontempi. (2017). Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy. IEEE Transactions on Neural Networks and Learning Systems. 29(8). 3784–3797. 338 indexed citations breakdown →

Caelen, Olivier. (2017). A Bayesian interpretation of the confusion matrix. Annals of Mathematics and Artificial Intelligence. 81(3-4). 429–450. 131 indexed citations

10.

Pozzolo, Andrea Dal, Olivier Caelen, R. A. Johnson, & Gianluca Bontempi. (2015). Calibrating Probability with Undersampling for Unbalanced Classification. Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles). 2 indexed citations

11.

Pozzolo, Andrea Dal, et al.. (2014). Using HDDT to avoid instances propagation in unbalanced and evolving data streams. Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles). 588–594. 16 indexed citations

12.

Wolff, Éléonore, et al.. (2010). Detecting man-made structure changes to assist geographic data producers in planning their update strategy. Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles). 38. 26–33. 7 indexed citations

13.

Caelen, Olivier, et al.. (2010). Detecting man-made structure changes to assist geographic data producers in planning their update strategy, ISPRS. Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles). 2 indexed citations

14.

Caelen, Olivier & Gianluca Bontempi. (2010). A dynamic programming strategy to balance exploration and exploitation in the bandit problem. Annals of Mathematics and Artificial Intelligence. 60(1-2). 3–24. 4 indexed citations

15.

Bontempi, Gianluca, Olivier Caelen, & Cécile Goffaux. (2009). On the use of supervised learning techniques to speed up the design of aeronautics components. Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles).

16.

Caelen, Olivier & Gianluca Bontempi. (2009). Sélection séquentielle en environnement aléatoire appliquée à l'apprentissage supervisé. Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles). 1 indexed citations

17.

Caelen, Olivier & Gianluca Bontempi. (2008). On the evolution of the expected gain of a greedy action in the bandit problem. Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles). 1 indexed citations

18.

Caelen, Olivier, et al.. (2006). Machine Learning Techniques to Enable Closed-Loop Control in Anesthesia. Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles). 696–701. 2 indexed citations

19.

Caelen, Olivier, et al.. (2006). Retrospective comparison of manual versus semi-automated propofol-remifentanil TCI Anaesthesia. Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles).

20.

Meyer, Patrick, Olivier Caelen, & Gianluca Bontempi. (2005). Speeding up Feature Selection by Using an Information Theoretic Bound.. Open Repository and Bibliography (University of Liège). 166–173. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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