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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林建甫(Chien-Fu Lin) | |
| dc.contributor.author | Che-Wei Kuo | en |
| dc.contributor.author | 郭哲瑋 | zh_TW |
| dc.date.accessioned | 2021-06-16T02:41:39Z | - |
| dc.date.available | 2020-08-25 | |
| dc.date.copyright | 2020-08-25 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-04 | |
| dc.identifier.citation | 陳獻儀、楊淑玲、張巧宜、蕭宇倫(2019)，「外部信評報告對P2P借貸平台是否具有資訊價值?」。管理學報，36(1)，29-51。 Barddal, J.P., Enembreck, F.,Ferreira, L.E.B., Gomes, H.M.(2017), “Improving Credit Risk Prediction in Online Peer-to-Peer (P2P) Lending Using Imbalanced Learning Techniques,”IEEE International Conference on, pp. 175–181. Beque, A., Lessmann, S.(2017), “Extreme learning machines for credit scoring : An empirical evaluation,”Expert Systems with Applications , 86, pp. 42–53. Breiman, L. (2001), “Random Forests,” Machine Learning, Vol. 45, No. 1, pp. 5-32. Chen, T. Q. Guestrin, C. (2016), “XGBoost: A Scalable Tree Boosting System,” KDD '16 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794. Chinmayi, K. M., Keerthana, S., Lakshmi, N., Natarajan, S. Vinod Kumar, L. (2016), “Credit Risk Analysis in Peer-to-Peer Lending System,”IEEE International Conference, pp. 193–196. Cudeck, R., O’Dell, L. L.(1994), “Applications of standard error estimates in unrestricted factor analysis: significance tests for factor loadings and correlations,” Psychological Bulletin, 115(3), pp. 475– 487. Duchi, J., Hazan, E. Singer, Y. (2011), “Adaptive Subgradient Methods for Online Learning and Stochastic Optimization,” Journal of Machine Learning Research, Vol. 12,pp. 2121–2159. Emekter, R., Tu, Y., Jirasakuldech, B., Lu, M. (2015), “Evaluating credit risk and loan performance in online peer-to-peer (P2P) lending,” Applied Economics, 47(1),pp. 54-70. Georganos,S., Grippa, T., Kalogirou, S., Lennert, M., Shimoni, M., Vanhuysse, S., Wolff, E.(2018), “Less is more: optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application,”GISci. Remote Sens, 55, pp. 221–242. ElNoeeff, I. Guyon, I.(2003), “An Introduction to Variable and Feature Selection,”The Journal of Machine Learning Research, Vol. 3, pp. 1157-1182. He, H., Zhang, W., Zhang, S. (2018), “A novel ensemble method for credit scoring: Adaption of different imbalance ratios,” Expert Systems with Applications, 98,pp. 105–117. Ho, T. K. (1995), “Random Decision Forest,” Proceeding of the 3rd International Conference on Document Analysis and Recognition, pp. 278-282. Ho, T. K. (1998), “The Random Subspace Method for Constructing Decision Forests,” IEEE Trans on Pattern Analysis and Machine Intelligence, Vol. 20, No. 8, pp. 832-844. Jović, A., Brkić, K., Bogunović, N. (2015), “A review of feature selection methods with appliciation,” MIPRO Conf., Rijeka, Croatia, MIPRO Croatian Society, pp. 1447-1452. Kang, H. (2013), “The Prevention and Handling of the Missing Data,” Korean Journal of Anesthesiology, Vol. 64, No. 5, pp. 402-406. Li, R., Li, X., Xing, C., Zhao, C., Zhang, G., Zhang, Y.(2018), “Comparative Analysis of Medical P2P for Credit Scores,”Web Information Systems and Applications, 11242, pp. 307–313. de Mendonca, A., Guerreiro, M., Maroco, J., Rodrigues, A., Silva, D. Santana, I.,(2011), “Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests,”BMC Res., 4(1), p. 299. Aksakalli, V. Malekipirbazari, M.(2015), “Risk assessment in social lending via random forests,”, Expert Systems with Applications, 42(10), pp. 4621–4631. Mester, L. J. (1997), “What’s the Point of Credit Scoring?” Business Review, No. 3, pp. 3-16. Milne, A. Parboteeah, P. (2016), “The Business Models and Economics of Peer-to-Peer Lending,” ECRI Research Report, No. 17. Naderpour, M., Namvar, A., Rabhi, F. Siami, M.(2018), “Credit risk prediction in an imbalanced social lending environment,”, International Journal of Computational Intelligence Systems, 11(1), pp. 925–935. Patro, S.G.K. and Sahu, K.K. (2015), “Normalization: A Preprocessing Stage,” [online] http://arxiv.org/ftp/arxiv/papers/1503/1503.06462.pdf (accessed 15 August 2015). Qian, N. (1999), “On the Momentum Term in Gradient Descent Learning Algorithms,” The Official Journal of the International Neural Network Society, Vol. 12, No.1, pp.145–151 Quinlan, J. R. (1987), “Simplifying Decision Trees.” International Journal of Man-Machine Studies, Vol. 27, No. 3,pp. 221-234. Samitsu, (2017), “The Structure of P2P Lending and Legal Arrangements: Focusing on P2P Lending Regulation in the UK,” IMES Discussion Paper Series, No. 17-J-3. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/54139 | - |
| dc.description.abstract | P2P lending是一種私人間透過網路平台的資金借貸方式，任何自然人、法人或非法人組織，皆可透過P2P業者提供的資訊與媒合，提供或獲取資金並從中獲取經濟上的利益。在台灣，透過銀行取得貸款雖非難事，但仍有許多被銀行排除在外的可能，這種實際上的現狀，使得手續便捷又不受地域、時間限制的P2P平臺興起，也讓人對它的未來走向產生濃厚的興趣與關注。
本研究以羅吉斯迴歸、基本決策樹、隨機森林、梯度提升決策樹、自我適應增強法、極限梯度提升樹與Catboost Classifier共7種演算法來進行風險控制的最佳模型分析，並重複進行多次驗證使得結果可以有穩定的表現；經過比較後，以綜合表現上以Catboost Classifier的表現為最佳算法。 | zh_TW |
| dc.description.abstract | P2P lending is a way of borrowing and lending funds between private individuals through an online platform. Any natural person, legal person or unincorporated organization can provide or get funds and receive economic benefits from the intermediaries provided by P2P providers. Although it is not difficult to get loans through banks in Taiwan, some limitations still exist. Those limitations gave rise to P2P platforms which are convenient in procedures and not subject to geographic or time constraints. Therefore, the future of P2P lending markets is worth expecting and paying attention to.
This study uses a total of 7 algorithms, including Logistic Regression, Basic Decision Tree, Random Forest, Gradient Boosting Decision Tree, Adaboost Classifier, XGB Classifier and Catboost Classifier to perform the best model for risk control, and improved by repeated verifications. After comparison, Catboost Classifier performed the best result. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T02:41:39Z (GMT). No. of bitstreams: 1 U0001-0308202022471200.pdf: 1997726 bytes, checksum: cbc60c3c9bd912b117d095ae7c27d5d8 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 口試委員會審定書 i 致謝 ii 中文摘要 iii ABSTRACT iv 目錄 v 圖目錄 vii 表格目錄 viii 第一章 緒論 1 第一節 研究動機 1 第二節 研究目的 3 第三節 研究流程 5 第二章 文獻回顧 6 第一節 P2P市場研究 6 第二節 P2P違約率相關文獻 7 第三章 研究方法 9 第一節 羅吉斯迴歸(Logistic) 9 第二節 基本決策樹(Decision Tree) 12 第三節 梯度提升決策樹(Gredient Boosting Decision Tree) 14 第四節 隨機森林(Radom Forest) 14 第五節 自我適應增強法(Adaboost) 15 第六節 極限梯度提升樹 (XGBoost) 16 第七節 Catboost 17 第四章 資料敘述統計與資料處理 20 第一節 借款原因 20 第二節 借款利率 21 第三節 特徵工程 23 第四節 移除貸後資訊 26 第五節 高度相關特徵移除 28 第六節 加入總體變數 29 第五章 實證結果 31 第一節 模型準確率與效率 31 第二節 模型分類指標 31 第六章 結論與建議 37 參考文獻 39 | |
| dc.language.iso | zh-TW | |
| dc.title | P2P借貸之違約率預測：應用機器學習 | zh_TW |
| dc.title | Predicting the Default Rates for P2P Lending：An Application of Machine Learning | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 杜震華(Jenn-Hwa Tu),林世昌(Shih-Chang Lin),郭平欣(Ping-Sing Kuo) | |
| dc.subject.keyword | 機器學習,P2P借貸,金融科技,違約預測,特徵工程, | zh_TW |
| dc.subject.keyword | Machine Learning,P2P Lending,Fintech,Default Rate Prediction,Feature Engineering, | en |
| dc.relation.page | 41 | |
| dc.identifier.doi | 10.6342/NTU202002327 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-08-05 | |
| dc.contributor.author-college | 社會科學院 | zh_TW |
| dc.contributor.author-dept | 經濟學研究所 | zh_TW |
| 顯示於系所單位： | 經濟學系 | |
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