Fraud Detection Using Machine Learning Techniques

.................................................................................................................. 2 Table of

[1]  Yunqian Ma,et al.  Imbalanced Datasets: From Sampling to Classifiers , 2013 .

[2]  Quan Pan,et al.  Random Decision Forests for Object Detection , 2014 .

[3]  Piyaphol Phoungphol,et al.  A Classification Framework for Imbalanced Data , 2013 .

[4]  C. Anagnostopoulos.  Measuring classification performance : the hmeasure package , 2019 .

[5]  Xin Yao,et al.  MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning , 2014 .

[6]  Zhi-Hua Zhou,et al.  Ensemble Methods: Foundations and Algorithms , 2012 .

[7]  Haibo He,et al.  ADASYN: Adaptive synthetic sampling approach for imbalanced learning , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[8]  Nitesh V. Chawla,et al.  Data Mining for Imbalanced Datasets: An Overview , 2005, The Data Mining and Knowledge Discovery Handbook.

[9]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[10]  David J. Hand,et al.  Measuring classifier performance: a coherent alternative to the area under the ROC curve , 2009, Machine Learning.

[11]  Maumita Bhattacharya,et al.  Intelligent Financial Fraud Detection: A Comprehensive Review , 2015 .

[12]  Takaya Saito,et al.  The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets , 2015, PloS one.

[13]  Christoforos Anagnostopoulos,et al.  A better Beta for the H measure of classification performance , 2012, Pattern Recognit. Lett..

[14]  E. A. Lopez-Rojas.  Applying Simulation to the Problem of Detecting Financial Fraud , 2016 .

[15]  Stefan Axelsson,et al.  Paysim: a financial mobile money simulator for fraud detection , 2016 .

[16]  Gianluca Bontempi,et al.  Racing for Unbalanced Methods Selection , 2013, IDEAL.

[17]  Nathalie Japkowicz,et al.  The class imbalance problem: A systematic study , 2002, Intell. Data Anal..

[18]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[19]  Hui Han,et al.  Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , 2005, ICIC.

[20]  S. Rigatti.  Random Forest. , 2017, Journal of insurance medicine.

[21]  Zhi-Hua Zhou,et al.  Exploratory Undersampling for Class-Imbalance Learning , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  M. McHugh.  Interrater reliability: the kappa statistic , 2012, Biochemia medica.

[23]  Daniel Berrar,et al.  On the Noise Resilience of Ranking Measures , 2016, ICONIP.

[24]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[25]  Peter Beling,et al.  Deep learning detecting fraud in credit card transactions , 2018, 2018 Systems and Information Engineering Design Symposium (SIEDS).