Improving Credit Risk Prediction in Online Peer-to-Peer (P2P) Lending Using Imbalanced Learning Techniques

作者: Luis Eduardo Boiko Ferreira , Jean Paul Barddal , Heitor Murilo Gomes , Fabricio Enembreck

DOI: 10.1109/ICTAI.2017.00037

关键词:

摘要: … In this work, we wrangle a real-world P2P lending data set from Lending Club, containing a large amount of data gathered from 2007 up to 2016. We analyze how supervised …

参考文章(33)
Max Bramer, Avoiding Overfitting of Decision Trees Principles of Data Mining. pp. 121- 136 ,(2013) , 10.1007/978-1-4471-4884-5_9
Charles Elkan, The foundations of cost-sensitive learning international joint conference on artificial intelligence. pp. 973- 978 ,(2001)
Bo Yuan, Xiaoli Ma, Sampling + reweighting: Boosting the performance of AdaBoost on imbalanced datasets international joint conference on neural network. pp. 1- 6 ,(2012) , 10.1109/IJCNN.2012.6252738
Milad Malekipirbazari, Vural Aksakalli, Risk assessment in social lending via random forests Expert Systems With Applications. ,vol. 42, pp. 4621- 4631 ,(2015) , 10.1016/J.ESWA.2015.02.001
Yoav Freund, Robert E Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting conference on learning theory. ,vol. 55, pp. 119- 139 ,(1997) , 10.1006/JCSS.1997.1504
Gustavo E. A. P. A. Batista, Ronaldo C. Prati, Maria Carolina Monard, A study of the behavior of several methods for balancing machine learning training data ACM SIGKDD Explorations Newsletter. ,vol. 6, pp. 20- 29 ,(2004) , 10.1145/1007730.1007735
Lior Rokach, Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography Computational Statistics & Data Analysis. ,vol. 53, pp. 4046- 4072 ,(2009) , 10.1016/J.CSDA.2009.07.017
YANMIN SUN, ANDREW K. C. WONG, MOHAMED S. KAMEL, CLASSIFICATION OF IMBALANCED DATA: A REVIEW International Journal of Pattern Recognition and Artificial Intelligence. ,vol. 23, pp. 687- 719 ,(2009) , 10.1142/S0218001409007326
Radha Vedala, Bandaru Rakesh Kumar, An application of Naive Bayes classification for credit scoring in e-lending platform international conference on data science and engineering. pp. 81- 84 ,(2012) , 10.1109/ICDSE.2012.6282321