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## Background Card fraud
@MISC{ECB2020,
author = "European Central Bank",
title = "6th report on card fraud",
month = "August",
year = "2020",
url = "\url{https://www.ecb.europa.eu/pub/cardfraud/html/ecb.cardfraudreport202008~521edb602b.en.html#toc2}",
note = "[Online; Last consulted 09-October-2020]",
}
@MISC{NilsonReport2019,
author = "Nilson report",
title = "Nilson report issue 1164",
month = "November",
year = "2019",
url = "\url{https://nilsonreport.com/upload/content_promo/The_Nilson_Report_Issue_1164.pdf }",
note = "[Online; Last consulted 09-October-2020]",
}
@MISC{StatisticBrain2018,
author = "Statistic Brain Research Institute",
title = "Credit Card Fraud Statistics",
month = "April",
year = "2018",
url = "\url{https://www.statisticbrain.com/credit-card-fraud-statistics/}",
note = "[Online; Last consulted 30-March-2021]",
}
## Background ML and credit card fraud
@article{chaudhary2012review,
title={A review of fraud detection techniques: Credit card},
author={Chaudhary, Khyati and Yadav, Jyoti and Mallick, Bhawna},
journal={International Journal of Computer Applications},
volume={45},
number={1},
pages={39--44},
year={2012},
publisher={Citeseer}
}
@article{dal2014learned,
title={Learned lessons in credit card fraud detection from a practitioner perspective},
author={Dal Pozzolo, Andrea and Caelen, Olivier and Le Borgne, Yann-Ael and Waterschoot, Serge and Bontempi, Gianluca},
journal={Expert systems with applications},
volume={41},
number={10},
pages={4915--4928},
year={2014},
publisher={Elsevier}
}
@article{NGAI2011559,
title = "The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature",
journal = "Decision Support Systems",
volume = "50",
number = "3",
pages = "559 - 569",
year = "2011",
note = "On quantitative methods for detection of financial fraud",
issn = "0167-9236",
doi = "https://doi.org/10.1016/j.dss.2010.08.006",
url = "http://www.sciencedirect.com/science/article/pii/S0167923610001302",
author = "E.W.T. Ngai and Yong Hu and Y.H. Wong and Yijun Chen and Xin Sun",
keywords = "Financial fraud, Fraud detection, Literature review, Data mining, Business intelligence",
abstract = "This paper presents a review of — and classification scheme for — the literature on the application of data mining techniques for the detection of financial fraud. Although financial fraud detection (FFD) is an emerging topic of great importance, a comprehensive literature review of the subject has yet to be carried out. This paper thus represents the first systematic, identifiable and comprehensive academic literature review of the data mining techniques that have been applied to FFD. 49 journal articles on the subject published between 1997 and 2008 was analyzed and classified into four categories of financial fraud (bank fraud, insurance fraud, securities and commodities fraud, and other related financial fraud) and six classes of data mining techniques (classification, regression, clustering, prediction, outlier detection, and visualization). The findings of this review clearly show that data mining techniques have been applied most extensively to the detection of insurance fraud, although corporate fraud and credit card fraud have also attracted a great deal of attention in recent years. In contrast, we find a distinct lack of research on mortgage fraud, money laundering, and securities and commodities fraud. The main data mining techniques used for FFD are logistic models, neural networks, the Bayesian belief network, and decision trees, all of which provide primary solutions to the problems inherent in the detection and classification of fraudulent data. This paper also addresses the gaps between FFD and the needs of the industry to encourage additional research on neglected topics, and concludes with several suggestions for further FFD research."
}
@article{zojaji2016survey,
title={A survey of credit card fraud detection techniques: data and technique oriented perspective},
author={Zojaji, Zahra and Atani, Reza Ebrahimi and Monadjemi, Amir Hassan and others},
journal={arXiv preprint arXiv:1611.06439 },
year={2016}
}
@inproceedings{lopez2016review,
title={A review of computer simulation for fraud detection research in financial datasets},
author={Lopez-Rojas, Edgar Alonso and Axelsson, Stefan},
booktitle={2016 Future Technologies Conference (FTC)},
pages={932--935},
year={2016},
organization={IEEE}
}
@article{adewumi2017survey,
title={A survey of machine-learning and nature-inspired based credit card fraud detection techniques},
author={Adewumi, Aderemi O and Akinyelu, Andronicus A},
journal={International Journal of System Assurance Engineering and Management},
volume={8},
number={2},
pages={937--953},
year={2017},
publisher={Springer}
}
@inproceedings{popat2018survey,
title={A survey on credit card fraud detection using machine learning},
author={Popat, Rimpal R and Chaudhary, Jayesh},
booktitle={2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI)},
pages={1120--1125},
year={2018},
organization={IEEE}
}
@inproceedings{sinayobye2018state,
title={A state-of-the-art review of machine learning techniques for fraud detection research},
author={Sinayobye, Janvier Omar and Kiwanuka, Fred and Kyanda, Swaib Kaawaase},
booktitle={2018 IEEE/ACM Symposium on Software Engineering in Africa (SEiA)},
pages={11--19},
year={2018},
organization={IEEE}
}
@article{mekterovic2018systematic,
title={A systematic review of data mining approaches to credit card fraud detection},
author={Mekterovi\'c, Igor and Brki\'c, Ljiljana and Baranovi\'c, Mirta},
journal={WSEAS Transactions on Business and Economics},
volume={15},
pages={437--444},
year={2018}
}
@article{sadgali2018detection,
title={Detection of credit card fraud: State of art},
author={Sadgali, Imane and Sael, Nawal and Benabbou, Faouzia},
journal={International Journal of computer science and network security},
volume={18},
number={11},
pages={76--83},
year={2018}
}
@article{patil2018survey,
title={A survey on different data mining \& machine learning methods for credit card fraud detection},
author={Patil, Vipul and Lilhore, Umesh Kumar},
journal={International Journal of Scientific Research in Computer Science, Engineering and Information Technology},
volume={3},
number={5},
pages={320--325},
year={2018}
}
@article{yousefi2019comprehensive,
title={A Comprehensive Survey on Machine Learning Techniques and User Authentication Approaches for Credit Card Fraud Detection},
author={Yousefi, Niloofar and Alaghband, Marie and Garibay, Ivan},
journal={arXiv preprint arXiv:1912.02629},
year={2019}
}
@inproceedings{priscilla2019credit,
title={Credit Card Fraud Detection: A Systematic Review},
author={Priscilla, C Victoria and Prabha, D Padma},
booktitle={International Conference on Information, Communication and Computing Technology},
pages={290--303},
year={2019},
organization={Springer}
}
@article{lucas2020credit,
title={Credit card fraud detection using machine learning: A survey},
author={Lucas, Yvan and Jurgovsky, Johannes},
journal={arXiv preprint arXiv:2010.06479},
year={2020}
}
## ML, more general
@book{friedman2001elements,
title={The elements of statistical learning},
author={Friedman, Jerome and Hastie, Trevor and Tibshirani, Robert},
volume={1},
number={10},
year={2001},
publisher={Springer series in statistics New York}
}
@book{bishop2006pattern,
title={Pattern recognition and machine learning},
author={Bishop, Christopher M},
year={2006},
publisher={springer}
}
@book{bontempi2021statistical,
title={Statistical foundations of machine learning, 2nd edition},
author={Bontempi, Gianluca},
publisher={Universit{\'e} Libre de Bruxelles},
year={2021}
}
## Python/Data science
@book{muller2016introduction,
title={Introduction to machine learning with Python: a guide for data scientists},
author={M{\"u}ller, Andreas C and Guido, Sarah},
year={2016},
publisher={O'Reilly Media, Inc.}
}
@book{mckinney2017python,
title={Python for data analysis: Data wrangling with Pandas, NumPy, and IPython - 2nd Edition},
author={McKinney, Wes},
year={2017},
publisher={O'Reilly Media, Inc.}
}
## MLG publications
@book{leborgne2022fraud,
title={Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook},
author={Le Borgne, Yann-A{\"e}l and Siblini, Wissam and Lebichot, Bertrand and Bontempi, Gianluca},
url={https://github.com/Fraud-Detection-Handbook/fraud-detection-handbook},
year={2022},
publisher={Universit{\'e} Libre de Bruxelles}
}
@inproceedings{siblini2021transfer,
author = {Wissam Siblini and
Guillaume Coter and
R{\'{e}}my Fabry and
Liyun He{-}Guelton and
Fr{\'{e}}d{\'{e}}ric Obl{\'{e}} and
Bertrand Lebichot and
Yann{-}A{\"{e}}l Le Borgne and
Gianluca Bontempi},
title = {Transfer Learning for Credit Card Fraud Detection: {A} Journey from
Research to Production},
booktitle = {Proceedings of Data Science and Advanced Analytics (DSAA 2021) },
year = {2021},
url = {https://arxiv.org/abs/2107.09323},
}
@article{lebichot2021transfer,
title={Transfer Learning Strategies for Credit Card Fraud Detection},
author={Lebichot, Bertrand and Verhelst, Th{\'e}o and Le Borgne, Yann-A{\"e}l and He-Guelton, Liyun and Obl{\'e}, Fr{\'e}d{\'e}ric and Bontempi, Gianluca},
journal={IEEE access},
volume={9},
pages={114754--114766},
year={2021},
publisher={IEEE}
}
@article{lebichot2021incremental,
title={Incremental learning strategies for credit cards fraud detection},
author={Lebichot, Bertrand and Paldino, Gian Marco and Siblini, W and He-Guelton, L and Obl{\'e}, F and Bontempi, G},
journal={International Journal of Data Science and Analytics},
pages={1--10},
year={2021},
publisher={Springer}
}
@article{carcillo2019combining,
title={Combining unsupervised and supervised learning in credit card fraud detection},
author={Carcillo, Fabrizio and Le Borgne, Yann-A{\"e}l and Caelen, Olivier and Kessaci, Yacine and Obl{\'e}, Fr{\'e}d{\'e}ric and Bontempi, Gianluca},
journal={Information Sciences},
year={2019},
publisher={Elsevier}
}
@inproceedings{lebichot2019deep,
title={Deep-learning domain adaptation techniques for credit cards fraud detection},
author={Lebichot, Bertrand and Le Borgne, Yann-A{\"e}l and He-Guelton, Liyun and Obl{\'e}, Fr{\'e}d{\'e}ric and Bontempi, Gianluca},
booktitle={INNS Big Data and Deep Learning conference},
pages={78--88},
year={2019},
organization={Springer}
}
@article{carcillo2018streaming,
title={Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization},
author={Carcillo, Fabrizio and Le Borgne, Yann-A{\"e}l and Caelen, Olivier and Bontempi, Gianluca},
journal={International Journal of Data Science and Analytics},
volume={5},
number={4},
pages={285--300},
year={2018},
publisher={Springer}
}
@book{carcillo2018beyond,
title={Beyond Supervised Learning in Credit Card Fraud Detection: A Dive into Semi-supervised and Distributed Learning},
author={Carcillo, Fabrizio},
year={2018},
publisher={Universit{\'e} libre de Bruxelles}
}
@article{carcillo2018scarff,
title={Scarff: a scalable framework for streaming credit card fraud detection with spark},
author={Carcillo, Fabrizio and Dal Pozzolo, Andrea and Le Borgne, Yann-A{\"e}l and Caelen, Olivier and Mazzer, Yannis and Bontempi, Gianluca},
journal={Information fusion},
volume={41},
pages={182--194},
year={2018},
publisher={Elsevier}
}
@article{dal2017credit,
title={Credit card fraud detection: a realistic modeling and a novel learning strategy},
author={Dal Pozzolo, Andrea and Boracchi, Giacomo and Caelen, Olivier and Alippi, Cesare and Bontempi, Gianluca},
journal={IEEE transactions on neural networks and learning systems},
volume={29},
number={8},
pages={3784--3797},
year={2017},
publisher={IEEE}
}
@book{dal2015adaptive,
title={Adaptive machine learning for credit card fraud detection},
author={Dal Pozzolo, Andrea},
year={2015},
publisher={Universit{\'e} libre de Bruxelles}
}
## Imbalance learning
@article{bentejac2021comparative,
title={A comparative analysis of gradient boosting algorithms},
author={Bent{\'e}jac, Candice and Cs{\"o}rgo, Anna and Mart{\'\i}nez-Mu{\~n}oz, Gonzalo},
journal={Artificial Intelligence Review},
volume={54},
number={3},
pages={1937--1967},
year={2021},
publisher={Springer}
}
@article{gupta2020class,
title={Class-Weighted Evaluation Metrics for Imbalanced Data Classification},
author={Gupta, Akhilesh and Tatbul, Nesime and Marcus, Ryan and Zhou, Shengtian and Lee, Insup and Gottschlich, Justin},
journal={arXiv preprint arXiv:2010.05995},
year={2020}
}
@article{ali2019review,
title={A review on data preprocessing methods for class imbalance problem},
author={Ali, Haseeb and Salleh, Mohd Najib Mohd and Hussain, Kashif and Ahmad, Arshad and Ullah, Ayaz and Muhammad, Arshad and Naseem, Rashid and Khan, Muzammil},
journal={International Journal of Engineering \& Technology},
volume={8},
pages={390--397},
year={2019}
}
@article{makki2019experimental,
title={An experimental study with imbalanced classification approaches for credit card fraud detection},
author={Makki, Sara and Assaghir, Zainab and Taher, Yehia and Haque, Rafiqul and Hacid, Mohand-Said and Zeineddine, Hassan},
journal={IEEE Access},
volume={7},
pages={93010--93022},
year={2019},
publisher={IEEE}
}
@book{fernandez2018learning,
title={Learning from imbalanced data sets},
author={Fern{\'a}ndez, Alberto and Garc{\'\i}a, Salvador and Galar, Mikel and Prati, Ronaldo C and Krawczyk, Bartosz and Herrera, Francisco},
year={2018},
publisher={Springer}
}
@article{dorogush2018catboost,
title={CatBoost: gradient boosting with categorical features support},
author={Dorogush, Anna Veronika and Ershov, Vasily and Gulin, Andrey},
journal={arXiv preprint arXiv:1810.11363},
year={2018}
}
@article{JMLR:v18:16-365,
author = {Guillaume Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas},
title = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning},
journal = {Journal of Machine Learning Research},
year = {2017},
volume = {18},
number = {17},
pages = {1-5},
url = {http://jmlr.org/papers/v18/16-365.html}
}
@article{last2017oversampling,
title={Oversampling for imbalanced learning based on k-means and smote},
author={Last, Felix and Douzas, Georgios and Bacao, Fernando},
journal={arXiv preprint arXiv:1711.00837},
year={2017}
}
@article{haixiang2017learning,
title={Learning from class-imbalanced data: Review of methods and applications},
author={Haixiang, Guo and Yijing, Li and Shang, Jennifer and Mingyun, Gu and Yuanyue, Huang and Bing, Gong},
journal={Expert Systems with Applications},
volume={73},
pages={220--239},
year={2017},
publisher={Elsevier}
}
@article{nguyen2011borderline,
title={Borderline over-sampling for imbalanced data classification},
author={Nguyen, Hien M and Cooper, Eric W and Kamei, Katsuari},
journal={International Journal of Knowledge Engineering and Soft Data Paradigms},
volume={3},
number={1},
pages={4--21},
year={2011},
publisher={Inderscience Publishers}
}
@article{KRIVKO20106070,
title = "A hybrid model for plastic card fraud detection systems",
journal = "Expert Systems with Applications",
volume = "37",
number = "8",
pages = "6070 - 6076",
year = "2010",
issn = "0957-4174",
doi = "https://doi.org/10.1016/j.eswa.2010.02.119",
url = "http://www.sciencedirect.com/science/article/pii/S0957417410001582",
author = "M. Krivko",
keywords = "Fraud detection, Hybrid model, Plastic card fraud, One-class classification",
abstract = "In this paper we present the framework for a hybrid model for plastic card fraud detection systems. The proposed data-customised approach combines elements of supervised and unsupervised methodologies aiming to compensate for the individual deficiencies of the methods. We demonstrate the ability of the hybrid model to identify fraudulent activity on the real debit card transaction data. We also explore the model’s efficiency against that of the existing monitoring system of the collaborating bank, using appropriate performance assessment criteria."
}
@article{yen2009cluster,
title={Cluster-based under-sampling approaches for imbalanced data distributions},
author={Yen, Show-Jane and Lee, Yue-Shi},
journal={Expert Systems with Applications},
volume={36},
number={3},
pages={5718--5727},
year={2009},
publisher={Elsevier}
}
@inproceedings{he2008adasyn,
title={ADASYN: Adaptive synthetic sampling approach for imbalanced learning},
author={He, Haibo and Bai, Yang and Garcia, Edwardo A and Li, Shutao},
booktitle={2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence)},
pages={1322--1328},
year={2008},
organization={IEEE}
}
@article{liu2008exploratory,
title={Exploratory undersampling for class-imbalance learning},
author={Liu, Xu-Ying and Wu, Jianxin and Zhou, Zhi-Hua},
journal={IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)},
volume={39},
number={2},
pages={539--550},
year={2008},
publisher={IEEE}
}
@article{chawla2008automatically,
title={Automatically countering imbalance and its empirical relationship to cost},
author={Chawla, Nitesh V and Cieslak, David A and Hall, Lawrence O and Joshi, Ajay},
journal={Data Mining and Knowledge Discovery},
volume={17},
number={2},
pages={225--252},
year={2008},
publisher={Springer}
}
@inproceedings{han2005borderline,
title={Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning},
author={Han, Hui and Wang, Wen-Yuan and Mao, Bing-Huan},
booktitle={International conference on intelligent computing},
pages={878--887},
year={2005},
organization={Springer}
}
@article{chawla2004special,
title={Special issue on learning from imbalanced data sets},
author={Chawla, Nitesh V and Japkowicz, Nathalie and Kotcz, Aleksander},
journal={ACM SIGKDD explorations newsletter},
volume={6},
number={1},
pages={1--6},
year={2004},
publisher={ACM New York, NY, USA}
}
@article{chen2004using,
title={Using random forest to learn imbalanced data},
author={Chen, Chao and Liaw, Andy and Breiman, Leo and others},
journal={University of California, Berkeley},
volume={110},
number={1-12},
pages={24},
year={2004}
}
@article{batista2004study,
title={A study of the behavior of several methods for balancing machine learning training data},
author={Batista, Gustavo EAPA and Prati, Ronaldo C and Monard, Maria Carolina},
journal={ACM SIGKDD explorations newsletter},
volume={6},
number={1},
pages={20--29},
year={2004},
publisher={ACM New York, NY, USA}
}
@inproceedings{batista2003balancing,
title={Balancing Training Data for Automated Annotation of Keywords: a Case Study.},
author={Batista, Gustavo EAPA and Bazzan, Ana LC and Monard, Maria Carolina and others},
booktitle={WOB},
pages={10--18},
year={2003}
}
@inproceedings{mani2003knn,
title={kNN approach to unbalanced data distributions: a case study involving information extraction},
author={Mani, Inderjeet and Zhang, I},
booktitle={Proceedings of workshop on learning from imbalanced datasets},
volume={126},
year={2003},
organization={ICML United States}
}
@article{chawla2002smote,
title={SMOTE: synthetic minority over-sampling technique},
author={Chawla, Nitesh V and Bowyer, Kevin W and Hall, Lawrence O and Kegelmeyer, W Philip},
journal={Journal of artificial intelligence research},
volume={16},
pages={321--357},
year={2002}
}
@article{friedman2001greedy,
title={Greedy function approximation: a gradient boosting machine},
author={Friedman, Jerome H},
journal={Annals of statistics},
pages={1189--1232},
year={2001},
publisher={JSTOR}
}
@inproceedings{laurikkala2001improving,
title={Improving identification of difficult small classes by balancing class distribution},
author={Laurikkala, Jorma},
booktitle={Conference on Artificial Intelligence in Medicine in Europe},
pages={63--66},
year={2001},
organization={Springer}
}
@inproceedings{provost2000machine,
title={Machine learning from imbalanced data sets 101},
author={Provost, Foster},
booktitle={Proceedings of the AAAI’2000 workshop on imbalanced data sets},
volume={68},
number={2000},
pages={1--3},
year={2000},
organization={AAAI Press}
}
@article{maclin1997empirical,
title={An empirical evaluation of bagging and boosting},
author={Maclin, Richard and Opitz, David},
journal={AAAI/IAAI},
volume={1997},
pages={546--551},
year={1997},
publisher={Citeseer}
}
@article{freund1997decision,
title={A decision-theoretic generalization of on-line learning and an application to boosting},
author={Freund, Yoav and Schapire, Robert E},
journal={Journal of computer and system sciences},
volume={55},
number={1},
pages={119--139},
year={1997},
publisher={Elsevier}
}
@article{tomek1976two,
title={Two modifications of CNN.},
journal={IEEE Trans. Syst. Man Commun},
volume={1},
pages={769--772},
author={Tomek, Ivan and others},
year={1976}
}
@article{wilson1972asymptotic,
title={Asymptotic properties of nearest neighbor rules using edited data},
author={Wilson, Dennis L},
journal={IEEE Transactions on Systems, Man, and Cybernetics},
number={3},
pages={408--421},
year={1972},
publisher={IEEE}
}
## Feature engineering
@article{whitrow2009transaction,
title={Transaction aggregation as a strategy for credit card fraud detection},
author={Whitrow, Christopher and Hand, David J and Juszczak, Piotr and Weston, David and Adams, Niall M},
journal={Data mining and knowledge discovery},
volume={18},
number={1},
pages={30--55},
year={2009},
publisher={Springer}
}
@article{VANVLASSELAER201538,
title={APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions},
author={Van Vlasselaer, V{\'e}ronique and Bravo, Cristi{\'a}n and Caelen, Olivier and Eliassi-Rad, Tina and Akoglu, Leman and Snoeck, Monique and Baesens, Bart},
journal={Decision Support Systems},
volume={75},
pages={38--48},
year={2015},
publisher={Elsevier}
}
## Anomaly detection
@article{AHMED2016278,
title = "A survey of anomaly detection techniques in financial domain",
journal = "Future Generation Computer Systems",
volume = "55",
pages = "278 - 288",
year = "2016",
issn = "0167-739X",
doi = "https://doi.org/10.1016/j.future.2015.01.001",
url = "http://www.sciencedirect.com/science/article/pii/S0167739X15000023",
author = "Mohiuddin Ahmed and Abdun Naser Mahmood and Md. Rafiqul Islam",
keywords = "Clustering, Fraud detection, Anomaly detection",
abstract = "Anomaly detection is an important data analysis task. It is used to identify interesting and emerging patterns, trends and anomalies from data. Anomaly detection is an important tool to detect abnormalities in many different domains including financial fraud detection, computer network intrusion, human behavioural analysis, gene expression analysis and many more. Recently, in the financial sector, there has been renewed interest in research on detection of fraudulent activities. There has been a lot of work in the area of clustering based unsupervised anomaly detection in the financial domain. This paper presents an in-depth survey of various clustering based anomaly detection technique and compares them from different perspectives. In addition, we discuss the lack of real world data and how synthetic data has been used to validate current detection techniques."
}
@article{zhang2019hoba,
title={HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture},
author={Zhang, Xinwei and Han, Yaoci and Xu, Wei and Wang, Qili},
journal={Information Sciences},
year={2019},
publisher={Elsevier}
}
@article{jones2019setting,
title={Setting the standards for machine learning in biology},
author={Jones, David T},
journal={Nature Reviews Molecular Cell Biology},
volume={20},
number={11},
pages={659--660},
year={2019},
publisher={Nature Publishing Group}
}
## Cost-sensitive
@article{ling2008cost,
title={Cost-sensitive learning and the class imbalance problem},
author={Ling, Charles X and Sheng, Victor S},
journal={Encyclopedia of machine learning},
volume={2011},
pages={231--235},
year={2008},
publisher={Citeseer}
}
## Accuracy metrics
@inproceedings{elkan2001foundations,
title={The foundations of cost-sensitive learning},
author={Elkan, Charles},
booktitle={International joint conference on artificial intelligence},
volume={17},
number={1},
pages={973--978},
year={2001},
organization={Lawrence Erlbaum Associates Ltd}
}
@article{fawcett2004roc,
title={ROC graphs: Notes and practical considerations for researchers},
author={Fawcett, Tom},
journal={Machine learning},
volume={31},
number={1},
pages={1--38},
year={2004}
}
@article{fawcett2006introduction,
title={An introduction to ROC analysis},
author={Fawcett, Tom},
journal={Pattern recognition letters},
volume={27},
number={8},
pages={861--874},
year={2006},
publisher={Elsevier}
}
@inproceedings{davis2006relationship,
title={The relationship between Precision-Recall and ROC curves},
author={Davis, Jesse and Goadrich, Mark},
booktitle={Proceedings of the 23rd international conference on Machine learning},
pages={233--240},
year={2006}
}
@incollection{chawla2009data,
title={Data mining for imbalanced datasets: An overview},
author={Chawla, Nitesh V},
booktitle={Data mining and knowledge discovery handbook},
pages={875--886},
year={2009},
publisher={Springer}
}
@article{fan2011detection,
title={Detection of rare items with target},
author={Fan, Guangzhe and Zhu, Mu},
journal={Statistics and Its Interface},
volume={4},
number={1},
pages={11--17},
year={2011},
publisher={International Press of Boston}
}
@inproceedings{boyd2013area,
title={Area under the precision-recall curve: point estimates and confidence intervals},
author={Boyd, Kendrick and Eng, Kevin H and Page, C David},
booktitle={Joint European conference on machine learning and knowledge discovery in databases},
pages={451--466},
year={2013},
organization={Springer}
}
@article{saito2015precision,
title={The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets},
author={Saito, Takaya and Rehmsmeier, Marc},
journal={PloS one},
volume={10},
number={3},
pages={e0118432},
year={2015},
publisher={Public Library of Science}
}
@inproceedings{flach2015precision,
title={Precision-recall-gain curves: PR analysis done right},
author={Flach, Peter and Kull, Meelis},
booktitle={Advances in neural information processing systems},
pages={838--846},
year={2015}
}
@article{muschelli2019roc,
title={Roc and auc with a binary predictor: a potentially misleading metric},
author={Muschelli, John},
journal={Journal of Classification},
pages={1--13},
year={2019},
publisher={Springer}
}
@article{tharwat2020classification,
title={Classification assessment methods},
author={Tharwat, Alaa},
journal={Applied Computing and Informatics},
year={2020},
publisher={Emerald Publishing Limited}
}
## Model selection
@article{cerqueira2020evaluating,
title={Evaluating time series forecasting models: an empirical study on performance estimation methods},
author={Cerqueira, Vitor and Torgo, Luis and Mozeti{\v{c}}, Igor},
journal={Machine Learning},
volume={109},
number={11},
pages={1997--2028},
year={2020},
publisher={Springer}
}
@article{gama2014survey,
title={A survey on concept drift adaptation},
author={Gama, Jo{\~a}o and {\v{Z}}liobait{\.e}, Indr{\.e} and Bifet, Albert and Pechenizkiy, Mykola and Bouchachia, Abdelhamid},
journal={ACM computing surveys (CSUR)},
volume={46},
number={4},
pages={1--37},
year={2014},
publisher={ACM New York, NY, USA}
}
@article{bergstra2012random,
title={Random search for hyper-parameter optimization.},
author={Bergstra, James and Bengio, Yoshua},
journal={Journal of machine learning research},
volume={13},
number={2},
year={2012}
}
## Datasets
@MISC{Kaggle2016,
author = "Kaggle",
title = "Credit Card Fraud Detection dataset",
month = "November",
year = "2016",
url = "\url{https://www.kaggle.com/mlg-ulb/creditcardfraud}",
note = "[Online; Last consulted 09-March-2021]",
}
## Libraries
@MISC{Imblearn,
author = "Imblearn",
title = "Imbalanced learning library for Python",
year = "2021",
url = "\url{https://imbalanced-learn.org/}",
note = "[Online; Last consulted 26-June-2021]",
}
## Cloud
@article{beg2021using,
title={Using Jupyter for reproducible scientific workflows},
author={Beg, Marijan and Taka, Juliette and Kluyver, Thomas and Konovalov, Alexander and Ragan-Kelley, Min and Thi{\'e}ry, Nicolas M and Fangohr, Hans},
journal={Computing in Science \& Engineering},
volume={23},
number={2},
pages={36--46},
year={2021},
publisher={IEEE}
}
## Deep learning
@MISC{kaggle2019fraud,
author = "Kaggle",
title = "IEEE-CIS Fraud Detection - Can you detect fraud from customer transactions?",
month = "September",
year = "2019",
url = "\url{https://www.kaggle.com/c/ieee-fraud-detection}",
note = "[Online; Last consulted 26-August-2021]",
}
@inproceedings{chen2016xgboost,
title={Xgboost: A scalable tree boosting system},
author={Chen, Tianqi and Guestrin, Carlos},
booktitle={Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining},
pages={785--794},
year={2016}
}
@article{ke2017lightgbm,
title={Lightgbm: A highly efficient gradient boosting decision tree},
author={Ke, Guolin and Meng, Qi and Finley, Thomas and Wang, Taifeng and Chen, Wei and Ma, Weidong and Ye, Qiwei and Liu, Tie-Yan},
journal={Advances in neural information processing systems},
volume={30},
pages={3146--3154},
year={2017}
}
@article{prokhorenkova2017catboost,
title={CatBoost: unbiased boosting with categorical features},
author={Prokhorenkova, Liudmila and Gusev, Gleb and Vorobev, Aleksandr and Dorogush, Anna Veronika and Gulin, Andrey},
journal={arXiv preprint arXiv:1706.09516},
year={2017}
}
@article{breiman2001random,
title={Random forests},
author={Breiman, Leo},
journal={Machine learning},
volume={45},
number={1},
pages={5--32},
year={2001},
publisher={Springer}
}
@inproceedings{domingos2000mining,
title={Mining high-speed data streams},
author={Domingos, Pedro and Hulten, Geoff},
booktitle={Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining},
pages={71--80},
year={2000}
}
@article{lakshminarayanan2014mondrian,
title={Mondrian forests: Efficient online random forests},
author={Lakshminarayanan, Balaji and Roy, Daniel M and Teh, Yee Whye},
journal={Advances in neural information processing systems},
volume={27},
pages={3140--3148},
year={2014}
}
@article{sun2018concept,
title={Concept drift adaptation by exploiting historical knowledge},
author={Sun, Yu and Tang, Ke and Zhu, Zexuan and Yao, Xin},
journal={IEEE transactions on neural networks and learning systems},
volume={29},
number={10},
pages={4822--4832},
year={2018},
publisher={IEEE}
}
@article{bahnsen2016feature,
title={Feature engineering strategies for credit card fraud detection},
author={Bahnsen, Alejandro Correa and Aouada, Djamila and Stojanovic, Aleksandar and Ottersten, Bj{\"o}rn},
journal={Expert Systems with Applications},
volume={51},
pages={134--142},
year={2016},
publisher={Elsevier}
}
@inproceedings{fu2016credit,
title={Credit card fraud detection using convolutional neural networks},
author={Fu, Kang and Cheng, Dawei and Tu, Yi and Zhang, Liqing},
booktitle={International conference on neural information processing},
pages={483--490},
year={2016},
organization={Springer}
}
@article{jurgovsky2018sequence,
title={Sequence classification for credit-card fraud detection},
author={Jurgovsky, Johannes and Granitzer, Michael and Ziegler, Konstantin and Calabretto, Sylvie and Portier, Pierre-Edouard and He-Guelton, Liyun and Caelen, Olivier},
journal={Expert Systems with Applications},
volume={100},
pages={234--245},
year={2018},
publisher={Elsevier}
}
@inproceedings{dastidar2020nag,
title={NAG: Neural feature aggregation framework for credit card fraud detection},
author={Dastidar, Kanishka Ghosh and Jurgovsky, Johannes and Siblini, Wissam and He-Guelton, Liyun and Granitzer, Michael},
booktitle={2020 IEEE International Conference on Data Mining (ICDM)},
pages={92--101},
year={2020},
organization={IEEE}
}
@article{konevcny2016federated,
title={Federated learning: Strategies for improving communication efficiency},
author={Kone{\v{c}}n{\`y}, Jakub and McMahan, H Brendan and Yu, Felix X and Richt{\'a}rik, Peter and Suresh, Ananda Theertha and Bacon, Dave},
journal={arXiv preprint arXiv:1610.05492},
year={2016}
}
@inproceedings{ghosh1994credit,
title={Credit card fraud detection with a neural-network},
author={Ghosh, Sushmito and Reilly, Douglas L},
booktitle={System Sciences, 1994. Proceedings of the Twenty-Seventh Hawaii International Conference on},
volume={3},
pages={621--630},
year={1994},
organization={IEEE}
}
@inproceedings{aleskerov1997cardwatch,
title={Cardwatch: A neural network based database mining system for credit card fraud detection},
author={Aleskerov, Emin and Freisleben, Bernd and Rao, Bharat},
booktitle={Proceedings of the IEEE/IAFE 1997 computational intelligence for financial engineering (CIFEr)},