請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88771完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 呂育道 | zh_TW |
| dc.contributor.advisor | Yuh-Dauh Lyuu | en |
| dc.contributor.author | 黃冠瑋 | zh_TW |
| dc.contributor.author | Guan-Wei Huang | en |
| dc.date.accessioned | 2023-08-15T17:43:12Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-15 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-02 | - |
| dc.identifier.citation | [1] V. Animah Ofosu-Hene. Literature reviews on loan default’s impact on ecobank finances. Journal of Engineering Applied Science and Humanities, 7(1):24–36, 2022.
[2] S.-C. Carlos, G.-N. Begoña, and L.-P. Luz. Determinants of default in P2P lending. PLoS ONE, 10:1–22, 2015. [3] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321–357, 2002. [4] K. Chinnapat, V. Nattakan, and S. Anantapom. A combination of decision tree learning and clustering for data classification. 2011 Eighth International Joint Conference on Computer Science and Software Engineering (JCSSE), pages 363–367, 2011. [5] R. Emekter, Y. Tu, B. Jirasakuldech, and M. Lu. Evaluating credit risk and loan performance in online peer-to-peer (P2P) lending. Applied Economics, 47(1):54–70, 2015. [6] T. Fawcett. An introduction to ROC analysis. Pattern Recognition Letters, 27(8):861–874, 2006. [7] H. Haibo and G. E. A. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9):1263–1284, 2009. [8] D. J. Hand and W. E. Henley. Statistical classification methods in consumer credit scoring: A review. Journal of the Royal Statistical Society. Series A (Statistics in Society), 160:523–541, 1997. [9] J. A. Hartigan and M. A. Wong. Algorithm as 136: A K-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28:100–108, 1979. [10] D. Jesse and G. Mark. The relationship between precision-recall and ROC curves. In Proceedings of the 23rd International Conference on Machine Learning, page 233–240, 2006. [11] X. Junhui, L. Zekai, and X. Ying. Loan default prediction of Chinese P2P market: a machine learning methodology. Scientific Reports, 11(1):18759, 2021. [12] S. Lessmann, B. Baesens, H.-V. Seow, and L. C. Thomas. Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247:124–136, 2015. [13] V. Moscato, A. Picariello, and G. Sperlí. A benchmark of machine learning approaches for credit score prediction. Expert Systems with Applications, 165:113986, 2021. [14] A. Namvar, M. Siami, F. Rabhi, and M. Naderpour. Credit risk prediction in an imbalanced social lending environment. International Journal of Computational Intelligence Systems, 11:925–935, 2018. [15] G. Nie, W. Rowe, L. Zhang, Y. Tian, and Y. Shi. Credit card churn forecasting by logistic regression and decision tree. Expert Systems with Applications, 38:15273–15285, 2011. [16] P. J. Rousseeuw. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20:53–65, 1987. [17] M. Roweida, R. Jumanah, and A. Malak. Machine learning with oversampling and undersampling techniques: Overview study and experimental results. In 2020 11th International Conference on Information and Communication Systems (ICICS), pages 243–248, 2020. [18] H.-W. Teng, M.-H. Kang, and I.-H. Lee. Improving credit scoring: A rescaled cluster-then-predict approach. Available at SSRN 4355268, 2023. [19] C.-F. Tsai. Combining cluster analysis with classifier ensembles to predict financial distress. Information Fusion, 16:46–58, 2014. [20] J. Xiao, Y. Tian, L. Xie, X. Jiang, and J. Huang. A hybrid classification framework based on clustering. IEEE Transactions on Industrial Informatics, 16(4):2177–2188, 2020. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88771 | - |
| dc.description.abstract | 在金融市場與貸款活動的發展下，對金融機構和借款人來說，預測貸款違約的機率是個極為重要的研究議題。本論文使用群聚演算法將資料根據相似性分成不同的群組，再依照分群結果建立分類器。根據實驗結果顯示，基於此種方式建立的預測系統可以有效的提高預測違約風險的準確性。另外本論文會透過選擇不同的特徵來分析其對違約預測的影響，發現同時使用借款人資料和貸款相關資料可以取得較好的表現。 | zh_TW |
| dc.description.abstract | With the development of financial markets and lending activities, predicting the probability of loan default has become a crucial research topic for financial institutions and lenders. This thesis utilizes clustering techniques for pre-classification and subsequently develops a classifier. The resulting system enhances the accuracy of default prediction. Additionally, this thesis examines the impact of different features for the prediction of default and finds that simultaneously utilizing borrower information and loan-related data achieves better performance. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-15T17:43:12Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-15T17:43:12Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員審定書 i
摘要 ii Abstract iii 目錄 iv 圖目錄 vi 表目錄 vii 第一章 緒論 1 1.1 研究動機 1 1.2 論文架構 2 第二章 背景知識 3 2.1 文獻回顧 3 2.2 Clustering 4 2.2.1 K-平均演算法 4 2.2.2 輪廓係數 5 2.3 不平衡資料 5 2.3.1 上採樣 5 2.3.2 下採樣 6 2.3.3 SMOTE 6 2.4 混淆矩陣 7 2.4.1 精確率 8 2.4.2 召回率 8 2.4.3 F1-score 8 2.4.4 ROC, AUC 9 第三章 實驗方法 10 3.1 實驗設計 10 3.2 資料來源及處理 11 3.3 資料分群 12 3.4 模型訓練 13 第四章 實驗結果 14 4.1 實驗一 15 4.2 實驗二 17 4.3 實驗三 20 第五章 結論與建議 23 5.1 結論 23 5.2 未來展望 24 參考文獻 25 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 貸款違約預測 | zh_TW |
| dc.subject | 不平衡資料 | zh_TW |
| dc.subject | 集成學習 | zh_TW |
| dc.subject | 分群 | zh_TW |
| dc.subject | 分類 | zh_TW |
| dc.subject | ensemble learning | en |
| dc.subject | imbalanced data | en |
| dc.subject | clustering | en |
| dc.subject | credit default prediction | en |
| dc.subject | classification | en |
| dc.title | 混合模型應用於貸款違約預測 | zh_TW |
| dc.title | Hybrid model for loan default prediction | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 陸裕豪;王釧茹;金國興 | zh_TW |
| dc.contributor.oralexamcommittee | U-Hou Lok;Chuan-Ju Wang;Gow-Hsing King | en |
| dc.subject.keyword | 貸款違約預測,集成學習,不平衡資料,分群,分類, | zh_TW |
| dc.subject.keyword | credit default prediction,ensemble learning,imbalanced data,clustering,classification, | en |
| dc.relation.page | 27 | - |
| dc.identifier.doi | 10.6342/NTU202301992 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-08-07 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊工程學系 | - |
| 顯示於系所單位： | 資訊工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-111-2.pdf 未授權公開取用 | 1.07 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。