98
Views
0
CrossRef citations to date
0
Altmetric
Articles

Feature selection in P2P lending based on hybrid genetic algorithm with machine learning

, , &
Pages 764-775 | Received 21 May 2023, Accepted 24 Oct 2023, Published online: 31 Oct 2023

References

  • Lee E, Lee B, Chae M. Herding behavior in online P2P lending: An empirical investigation. 7-11 July 2011. Brisbane, Australia. PACIS 2011 - 15th Pacific Asia Conference on Information Systems: Quality Research in Pacific. 2011.
  • Jin Y, Zhu Y. A Data-Driven Approach to Predict Default Risk of Loan for Online Peer-to-Peer (P2P) Lending. 04-06 April 2015. Gwalior, India. 2015 Fifth International Conference on Communication Systems and Network Technologies. 2015.
  • Byanjankar A, Heikkila M, Mezei J. Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach. 11 January 2016. Cape Town, South Africa. 2015 IEEE Symposium Series on Computational Intelligence. 2015.
  • Malekipirbazari M, Aksakalli V. Risk assessment in social lending via random forests. Expert Syst Appl. 2015;42(10):4621–4631. doi: 10.1016/j.eswa.2015.02.001
  • Li H, Zhang Y, Zhang N, et al. Detecting the abnormal lenders from P2P lending data. Procedia Comput Sci. 2016;91:357–361. doi: 10.1016/j.procs.2016.07.095
  • Serrano-Cinca C, Gutierrez-Nieto B. The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending. Decis Support Syst. 2016;89:113–122. doi: 10.1016/j.dss.2016.06.014
  • Yan J, Wang K, Liu Y, et al. Mining social lending motivations for loan project recommendations. Expert Syst Appl. 2018;111:100–106. doi: 10.1016/j.eswa.2017.11.010
  • Xia Y, Yang X, Zhang Y. A rejection inference technique based on contrastive pessimistic likelihood estimation for P2P lending. Electron Commer Res Appl. 2018;30:111–124. doi: 10.1016/j.elerap.2018.05.011
  • Madaan M, Kumar A, Keshri C, et al. Loan default prediction using decision trees and random forest: a comparative study. IOP Conf Ser: Materials Science and Engineering. 2021;1022(1):012042. doi: 10.1088/1757-899X/1022/1/012042
  • Sharma AK, Li LH, Ahmad R. Identifying and predicting default borrowers in P2P lending platform: A machine learning approach. 29-31 August 2021. Taichung, Taiwan. 2021 IEEE International Conference on Social Sciences and Intelligent Management (SSIM). 2021.
  • Tumuluru P, Burra LR, Loukya M. Comparative Analysis of Customer Loan Approval Prediction using Machine Learning Algorithms. 23-25 February 2022. Coimbatore, India. 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS). 2022.
  • Wang Y, Zhang Y, Lu Y, et al. A comparative assessment of credit risk model based on machine learning – a case study of bank loan data. Procedia Comput Sci. 2020;174:141–149. doi: 10.1016/j.procs.2020.06.069
  • Yamparala R, Saranya JR, Anusha P, et al. Predictıng the loan using machine learning. In: Soft computing for security applications. Advances in intelligent systems and computing. 2023. p. 701–712.
  • Munsarif M, Sam'an M, Safuan. Peer to peer lending risk analysis based on embedded technique and stacking ensemble learning. Bull Electrical Eng Inf. 2022;11(6):3483–3489.
  • Chen RC, Dewi C, Huang SW, et al. Selecting critical features for data classification based on machine learning methods. J Big Data. 2020;7(1):52. doi: 10.1186/s40537-020-00327-4
  • Al-Zoubi AM, Faris H, Alqatawna J, et al. Evolving support vector machines using whale optimization algorithm for spam profiles detection on online social networks in different lingual contexts. Knowl Based Syst. 2018;153:91–104. doi: 10.1016/j.knosys.2018.04.025
  • Kumar N, Singh AK, Srivastava S. Feature selection for interest flooding attack in named data networking. Int J Computers Appl. 2021;43(6):537–546.
  • Thiyam B, Dey S. Statistical methods for feature selection: unlocking the key to improved accuracy. International Journal of Computers and Applications. 2023;45:433–443. doi: 10.1080/1206212X.2023.2223795
  • Wang HD. Research on the features of car insurance data based on machine learning. Procedia Comput Sci. 2020;166:582–587. doi: 10.1016/j.procs.2020.02.016
  • Jain D, Singh V. A two-phase hybrid approach using feature selection and adaptive SVM for chronic disease classification. Int J Computers Appl. 2021;43(6):524–536.
  • Papouskova M, Hajek P. Two-stage consumer credit risk modelling using heterogeneous ensemble learning. Decis Support Syst. 2019;118:33–45. doi: 10.1016/j.dss.2019.01.002
  • Zheng X. Feature selection algorithm of network attack big data under the interference of fading noise. Int J Computers Appl. 2022;44(9):807–813.
  • Angadi S, Reddy VS. Multimodal sentiment analysis using reliefF feature selection and random forest classifier. Int J Comput Appl. 2021;43(9):931–939.
  • Fathima MD, Samuel SJ, Natchadalingam R, et al. Majority voting ensembled feature selection and customized deep neural network for the enhanced clinical decision support system. Int J Comput Appl. 2022;44(10):991–1001.
  • Gu S, Cheng R, Jin Y. Feature selection for high-dimensional classification using a competitive swarm optimizer. Soft Comput. 2018;22(3):811–822. doi: 10.1007/s00500-016-2385-6
  • Xu J, Chen D, Chau M. Identifying features for detecting fraudulent loan requests on P2P platforms. 28-30 September 2016.Tucson, AZ, USA. 2016 IEEE Conference on Intelligence and Security Informatics (ISI). 2016.
  • Ha V-S, Lu D-N, Choi GS. Improving Credit Risk Prediction in Online Peer-to-Peer (P2P) Lending Using Feature selection with Deep learning. 17-20 February 2019. PyeongChang, Korea (South). 2019 21st International Conference on Advanced Communication Technology (ICACT). 2019.
  • Li X, Ergu D, Zhang D. Prediction of loan default based on multi-model fusion. 9-11 july 2021. Chengdu, China. the 8th International Conference on Information Technology and Quantitative Management. 2021.
  • Yang R, Wang P, Qi J. A novel SSA-CatBoost machine learning model for credit rating. J Intell Fuzzy Syst. 2023;44(2):2269–2284. doi: 10.3233/JIFS-221652
  • Yin W, Kirkulak-Uludag B, Zhu D, et al. Stacking ensemble method for personal credit risk assessment in Peer-to-Peer lending. Appl Soft Comput. 2023;142:110302. doi: 10.1016/j.asoc.2023.110302
  • Cao W, He Y, Wang W, et al. Ensemble methods for credit scoring of Chinese peer-to-peer loans. J Credit Risk. 2021;17(3):79–115.
  • Victor L, Raheem M. Loan default prediction using genetic algorithm: a study within Peer-To-Peer lending communities. Int J Innovative Sci Res Technol. 2021;6(3):1195–1205.
  • Lappas PZ, Yannacopoulos AN. A machine learning approach combining expert knowledge with genetic algorithms in feature selection for credit risk assessment. Appl Soft Comput. 2021;107:107391. doi: 10.1016/j.asoc.2021.107391
  • Ye X, Dong L, Ma D. Loan evaluation in P2P lending based on random forest optimized by genetic algorithm with profit score. Electron Commer Res Appl. 2018;32:23–36. doi: 10.1016/j.elerap.2018.10.004
  • Zhu L, Qiu D, Ergu D, et al. A study on predicting loan default based on the random forest algorithm. Procedia Comput Sci. 2019;162:503–513. doi: 10.1016/j.procs.2019.12.017
  • Nguyen Truong T, Khuat Thanh S, Ngo Thi Thu T, et al. Improve risk prediction in online lending (P2P) using feature selection and deep learning. Int J Computer Sci Network Security. 2019;19(11):216–222.
  • Setiawan N, Suharjito D.. A comparison of prediction methods for credit default on peer to peer lending using machine learning. Procedia Comput Sci. 2019;157:38–45. doi: 10.1016/j.procs.2019.08.139
  • George N. All lending club loan data. Kaggle, 2019. Available at https://www.kaggle.com/datasets/wordsforthewise/lending-club/metadata.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.