An Investigation of Credit Card Default Prediction in the Imbalanced Datasets
暂无分享,去创建一个
Matloob Khushi | Ibrahim A. Hameed | Suhuai Luo | Jiaming Li | Talha Mahboob Alam | Kamran Shaukat | Muhammad Umer Sarwar | Shakir Shabbir | K. Shaukat | I. Hameed | S. Luo | Jiaming Li | Matloob Khushi | Shakir Shabbir
[1] Michal Tkác,et al. Artificial neural networks in business: Two decades of research , 2016, Appl. Soft Comput..
[2] Yi Peng,et al. Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending , 2020, J. Oper. Res. Soc..
[3] Seetha Hari,et al. Learning From Imbalanced Data , 2019, Advances in Computer and Electrical Engineering.
[4] Herbert Kimura,et al. Machine learning models and bankruptcy prediction , 2017, Expert Syst. Appl..
[5] Hussain Ali Bekhet,et al. Credit risk assessment model for Jordanian commercial banks : neural scoring approach , 2014 .
[6] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[7] Yong Hu,et al. The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature , 2011, Decis. Support Syst..
[8] Amalia Luque,et al. The impact of class imbalance in classification performance metrics based on the binary confusion matrix , 2019, Pattern Recognit..
[9] Kilian Q. Weinberger,et al. Gradient boosted feature selection , 2014, KDD.
[10] Francesco Ciampi,et al. Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms , 2015 .
[11] Hoon Cho,et al. An empirical study on credit card loan delinquency , 2018, Economic Systems.
[12] Gianluca Bontempi,et al. Learned lessons in credit card fraud detection from a practitioner perspective , 2014, Expert Syst. Appl..
[13] Yue-Shi Lee,et al. Cluster-based under-sampling approaches for imbalanced data distributions , 2009, Expert Syst. Appl..
[14] Yi Peng,et al. BEHAVIOR MONITORING METHODS FOR TRADE-BASED MONEY LAUNDERING INTEGRATING MACRO AND MICRO PRUDENTIAL REGULATION: A CASE FROM CHINA , 2019, Technological and Economic Development of Economy.
[15] Eric Séverin,et al. An investigation of bankruptcy prediction in imbalanced datasets , 2018, Decis. Support Syst..
[16] Yufei Xia,et al. Predicting loan default in peer‐to‐peer lending using narrative data , 2020, Journal of Forecasting.
[17] Sanmay Das,et al. Risk and Risk Management in the Credit Card Industry , 2015 .
[18] Dongxi Liu,et al. Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity , 2020, Energies.
[19] Muhammad Atif,et al. Cervical Cancer Prediction through Different Screening Methods using Data Mining , 2019, International Journal of Advanced Computer Science and Applications.
[20] Shulin Wang,et al. Feature selection in machine learning: A new perspective , 2018, Neurocomputing.
[21] I-Cheng Yeh,et al. The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients , 2009, Expert Syst. Appl..
[22] Fawwad Hassan Jaskani,et al. Comparison of Classification Models for Early Prediction of Breast Cancer , 2019, 2019 International Conference on Innovative Computing (ICIC).
[23] Gang Kou,et al. Retail investor attention and stock price crash risk: Evidence from China , 2019, International Review of Financial Analysis.
[24] Muhammad Shoaib Farooq,et al. Detection of Schistosomiasis Factors Using Association Rule Mining , 2019, IEEE Access.
[25] K. Maddulety,et al. Machine Learning in Banking Risk Management: A Literature Review , 2019, Risks.
[26] Jing Zhou,et al. Default prediction in P2P lending from high-dimensional data based on machine learning , 2019, Physica A: Statistical Mechanics and its Applications.
[27] Kamran Shaukat,et al. Student’s Performance: A Data Mining Perspective , 2017 .
[28] Talha Mahboob Alam,et al. Domain Analysis of Information Extraction Techniques , 2018 .
[29] Matloob Khushi,et al. Predicting High-Risk Prostate Cancer Using Machine Learning Methods , 2019, Data.
[30] Yi Peng,et al. MACHINE LEARNING METHODS FOR SYSTEMIC RISK ANALYSIS IN FINANCIAL SECTORS , 2019, Technological and Economic Development of Economy.
[31] Kamran Shaukat,et al. Student's performance in the context of data mining , 2016, 2016 19th International Multi-Topic Conference (INMIC).
[32] Yan Yu,et al. Financial ratios and bankruptcy predictions: An international evidence , 2017 .
[33] José Francisco Martínez Trinidad,et al. Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases , 2016, Neurocomputing.
[34] Zhijun Ding,et al. A hybrid interpretable credit card users default prediction model based on RIPPER , 2018, Concurr. Comput. Pract. Exp..
[35] Che Lin,et al. Enhanced Recurrent Neural Network for Combining Static and Dynamic Features for Credit Card Default Prediction , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[36] Mohamed Elhoseny,et al. Feature selection based on artificial bee colony and gradient boosting decision tree , 2019, Appl. Soft Comput..
[37] Kamran Shaukat,et al. A Socio-Technological analysis of Cyber Crime and Cyber Security in Pakistan , 2017 .
[38] Fernando Bação,et al. Oversampling for Imbalanced Learning Based on K-Means and SMOTE , 2017, Inf. Sci..
[39] Wei Li,et al. Transfer learning-based default prediction model for consumer credit in China , 2018, The Journal of Supercomputing.
[40] Manoj Jayabalan,et al. A Comparative Study on Credit Card Default Risk Predictive Model , 2019, Journal of Computational and Theoretical Nanoscience.
[41] Yi Peng,et al. Evaluation of clustering algorithms for financial risk analysis using MCDM methods , 2014, Inf. Sci..
[42] Meherwar Fatima,et al. Performance Comparison of Data Mining Algorithms for the Predictive Accuracy of Credit Card Defaulters , 2017 .
[43] Shigeyuki Hamori,et al. Ensemble Learning or Deep Learning? Application to Default Risk Analysis , 2018 .
[44] 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).
[45] Manzoor Ahmed Hashmani,et al. Performance analysis of feature selection algorithm for educational data mining , 2017, 2017 IEEE Conference on Big Data and Analytics (ICBDA).
[46] Yufei Xia,et al. A novel heterogeneous ensemble credit scoring model based on bstacking approach , 2018, Expert Syst. Appl..
[47] Jing Qiu,et al. Dynamic ensemble classification for credit scoring using soft probability , 2018, Appl. Soft Comput..
[48] Ana L. C. Bazzan,et al. Balancing Training Data for Automated Annotation of Keywords: a Case Study , 2003, WOB.
[49] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[50] Matloob Khushi,et al. Reinforcement Learning in Financial Markets , 2019, Data.
[51] Matloob Khushi,et al. Corporate Bankruptcy Prediction: An Approach Towards Better Corporate World , 2020, Comput. J..
[52] A. Lo,et al. Consumer Credit Risk Models Via Machine-Learning Algorithms , 2010 .