Credit Card Fraud Detection under Extreme Imbalanced Data: A Comparative Study of Data-level Algorithms
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[1] Arpit Singh,et al. A Survey on Methods for Solving Data Imbalance Problem for Classification , 2015 .
[2] Francisco Herrera,et al. A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[3] Tom Fawcett,et al. Adaptive Fraud Detection , 1997, Data Mining and Knowledge Discovery.
[4] Hewijin Christine Jiau,et al. Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem , 2006 .
[5] Yue-Shi Lee,et al. Cluster-based under-sampling approaches for imbalanced data distributions , 2009, Expert Syst. Appl..
[6] S Geetha,et al. Credit Card Fraud Detection using Machine Learning Algorithms , 2019, Procedia Computer Science.
[7] Longbing Cao,et al. Effective detection of sophisticated online banking fraud on extremely imbalanced data , 2012, World Wide Web.
[8] Thach Huy Nguyen,et al. Cost-Xensitive XCS Classifier System Addressing Imbalance Problems , 2008, 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery.
[9] R. Sekar,et al. Specification-based anomaly detection: a new approach for detecting network intrusions , 2002, CCS '02.
[10] Mingzhe Jin,et al. The Effects of Class Imbalance and Training Data Size on Classifier Learning: An Empirical Study , 2020, SN Comput. Sci..
[11] Roberto Alejo,et al. A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios , 2013, Pattern Recognit. Lett..
[12] Sara Makki,et al. An Experimental Study With Imbalanced Classification Approaches for Credit Card Fraud Detection , 2019, IEEE Access.
[13] Mohak Shah,et al. Evaluating Learning Algorithms: A Classification Perspective , 2011 .
[14] Herna L. Viktor,et al. Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach , 2004, SKDD.
[15] Ruttala Sailusha,et al. Credit Card Fraud Detection Using Machine Learning , 2020, 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS).
[16] Zhao Hai,et al. Learning from imbalanced data sets with a Min-Max modular support vector machine , 2011 .
[17] Edward Y. Chang,et al. KBA: kernel boundary alignment considering imbalanced data distribution , 2005, IEEE Transactions on Knowledge and Data Engineering.
[18] Jose Miguel Puerta,et al. Improving the performance of Naive Bayes multinomial in e-mail foldering by introducing distribution-based balance of datasets , 2011, Expert Syst. Appl..
[19] Damminda Alahakoon,et al. Minority report in fraud detection: classification of skewed data , 2004, SKDD.
[20] Jianjun Wang,et al. Margin calibration in SVM class-imbalanced learning , 2009, Neurocomputing.
[21] Benjamin Kuipers,et al. Process monitoring and diagnosis: a model-based approach , 1991, IEEE Expert.
[22] J. Ross Quinlan. Improved Estimates for the Accuracy of Small Disjuncts , 2005, Machine Learning.
[23] Wei-Zhen Lu,et al. Ground-level ozone prediction by support vector machine approach with a cost-sensitive classification scheme. , 2008, The Science of the total environment.
[24] Fauziah Baharom,et al. A Construction of Service-Oriented Architecture Adoption Maturity Levels using Adoption of Innovation Concept and CMMI , 2018 .
[25] José Salvador Sánchez,et al. On the effectiveness of preprocessing methods when dealing with different levels of class imbalance , 2012, Knowl. Based Syst..
[26] Francisco Herrera,et al. SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary , 2018, J. Artif. Intell. Res..
[27] Stan Matwin,et al. Machine Learning for the Detection of Oil Spills in Satellite Radar Images , 1998, Machine Learning.
[28] Xue-wen Chen,et al. Pruning support vectors for imbalanced data classification , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[29] Yun Zhang,et al. Parallel classifiers ensemble with hierarchical machine learning for imbalanced classes , 2008, 2008 International Conference on Machine Learning and Cybernetics.
[30] David P. Williams,et al. Mine Classification With Imbalanced Data , 2009, IEEE Geoscience and Remote Sensing Letters.
[31] L.M. Patnaik,et al. Genetic Algorithm with Characteristic Amplification through Multiple Geographically Isolated Populations and Varied Fitness Landscapes , 2007, 15th International Conference on Advanced Computing and Communications (ADCOM 2007).
[32] Mohammad Khalilia,et al. Predicting disease risks from highly imbalanced data using random forest , 2011, BMC Medical Informatics Decis. Mak..
[33] Kishan G. Mehrotra,et al. An improved algorithm for neural network classification of imbalanced training sets , 1993, IEEE Trans. Neural Networks.
[34] T.M. Padmaja,et al. Unbalanced data classification using extreme outlier elimination and sampling techniques for fraud detection , 2007, 15th International Conference on Advanced Computing and Communications (ADCOM 2007).
[35] Vadlamani Ravi,et al. Credit Card Fraud Detection using Big Data Analytics: Use of PSOAANN based One-Class Classification , 2016, ICIA.
[36] Salvatore J. Stolfo,et al. Distributed data mining in credit card fraud detection , 1999, IEEE Intell. Syst..
[37] Yi-Hung Liu,et al. Total margin based adaptive fuzzy support vector machines for multiview face recognition , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.
[38] Jayadev Gyani,et al. Class Imbalance Reduction (CIR): A Novel Approach to Software Defect Prediction in the Presence of Class Imbalance , 2020, Symmetry.
[39] Bianca Zadrozny,et al. Learning and making decisions when costs and probabilities are both unknown , 2001, KDD '01.
[40] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[41] Prashasti Kanikar,et al. Credit card Fraud Detection based on Machine Learning Algorithms , 2019, International Journal of Computer Applications.
[42] Zili Zhang,et al. Sample Subset Optimization Techniques for Imbalanced and Ensemble Learning Problems in Bioinformatics Applications , 2014, IEEE Transactions on Cybernetics.
[43] Francisco Herrera,et al. Learning from Imbalanced Data Sets , 2018, Springer International Publishing.
[44] Ali A. Ghorbani,et al. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS 1 Toward Credible Evaluation of Anomaly-Based Intrusion-Detection Methods , 2022 .
[45] J. L. Hodges,et al. Rank Methods for Combination of Independent Experiments in Analysis of Variance , 1962 .
[46] Yue-Shi Lee,et al. Investigating the Effect of Sampling Methods for Imbalanced Data Distributions , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.