應用深度學習於遺漏值填補在財務危機領域的影響分析

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：89

、訪客IP：52.14.8.34

姓名

曹皓閔(Hao-Min Tsao) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

應用深度學習於遺漏值填補在財務危機領域的影響分析
(The Effect Analysis of Missing Value Imputation on Financial Crisis Prediction via Deep Learning classification)

相關論文

★ 具代理人之行動匿名拍賣與付款機制	★ 網路攝影機遠端連線安全性分析
★ HSDPA環境下的複合式細胞切換機制	★ 樹狀結構為基礎之行動隨意網路IP位址分配機制
★ 平面環境中目標區域之偵測 - 使用行動感測網路技術	★ 藍芽Scatternet上的P2P檔案分享機制
★ 交通壅塞避免之動態繞路機制	★ 運用UWB提升MANET上檔案分享之效能
★ 合作學習平台對團體迷思現象及學習成效之影響–以英文字彙學習為例	★ 以RFID為基礎的室內定位機制─使用虛擬標籤的經驗法則
★ 適用於實體購物情境的行動商品比價系統-使用影像辨識技術	★ 信用卡網路刷卡安全性
★ DEAP:適用於行動RFID系統之高效能動態認證協定	★ 在破產預測與信用評估領域對前處理方式與分類器組合的比較分析
★ 單一類別分類方法於不平衡資料集－搭配遺漏值填補和樣本選取方法	★ 正規化與變數篩選在破產領域的適用性研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2025-7-1以後開放)

摘要(中)

在過往的許多研究已經對補值有許多的討論，但破產預測以及信用評估領域中被討論的較少，大多研究僅採用UCI資料集進行實驗或者僅在訓練資料集探討補值效果，另一方面，深度神經網路用於補值或進行分類預測在過往的研究中是較少被提及的，仍是一個未知且需要被探討的議題。
為了瞭解補值在破產預測與信用評估領域的適用性，本研究蒐集了五個信用資料集(澳洲信用、日本信用、德國信用、Kaggle和pakdd)與四個破產資料集(Bankruptcy、日本破產、TEJ台灣破產、美國破產)，搭配四種補值方法，K-鄰近算法(K-nearest neighbor)、隨機森林(Random Forest)、鏈式方程多重插補法(Multivariate Imputation by Chained Equations)和深度神經網路(Deep Neural Network)，並以四種不同的分類器，支持向量機(Support Vector Machine)、隨機森林(Random Forest)、深度神經網路(Deep Neural Network)和深度信賴網路搭疊深度神經網路(Deep Belief Network Stacked Deep Neural Network)進行分類，探討不同補值方法對於結果的影響。另外，進一步探討補值搭配正規化能否進一步提升預測的準確率。
本研究發現在整體平均下，補值可以提升分類的準確率，在補值搭配正規化能有效提升類神經網路的效果，並且對比神經網路與機器學習，經過正規化後神經網路顯著優於機器學習。經過正規化後所有實驗組合中，DBN-DNN都為最佳的分類器，在小遺漏時搭配RF補值可以獲得最佳AUC、搭配MICE則能得最佳的Type II；在大遺漏時搭配RF補值即為最佳AUC和Type II結果。

摘要(英)

In past studies, many had discussed about missing value imputation but most of them did experiments on UCI datasets or only on training datasets. Seldom does a study discuss about missing value on bankruptcy prediction and credit scoring. On the other hand, using deep neural network for imputation or classification prediction is rarely mentioned in past research, and is still an unknown and need to be discussed.
The applicability of missing value imputation on bankruptcy prediction and credit scoring is analyzed by using five credit datasets (Australia, Japan, Germany, Kaggle and pakdd) and four bankruptcy datasets (Bankruptcy, Japan Bankruptcy, TEJ Taiwan Bankruptcy and US Bankruptcy) with four imputation methods, including Deep Neural Network, K-nearest neighbor, Random Forest and Multivariate Imputation by Chained Equations, and at last using four different classifiers: Support Vector Machine, Random Forest, Deep Neural Network and Deep Belief Network Stacked Deep Neural Network respectively to discuss the effect of imputation methods on outcomes. Furthermore, this experiment also explore the possibility on whether data normalization will improve prediction accuracy.
This experiment finds out that on average, imputation improves the classification accuracy, and data normalization along with imputation, can elaborate the effect of artificial neural network. Besides, in comparison with machine learning, neural network performs much better after data normalization. In every pair of experiments after normalization, DBN-DNN outstands other classifiers. When missing rate is low, the combination with Random Forest outputs the best AUC, with MICE on the other hand, gets the lowest type II error. When missing rate is high, the combination with Random Forest takes the first place for the best AUC and the lowest type II error.

關鍵字(中)

★ 遺漏值填補
★ 破產預測
★ 信用評估
★ 類神經網路

關鍵字(英)

★ Missing Value Imputation
★ Bankruptcy Prediction
★ Credit Scoring
★ Artificial Neural Network

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 x
一、緒論 1
1-1 研究背景 1
1-2 研究動機 2
1-3 研究目的 4
1-4 研究架構 5
二、文獻探討 6
2-1 遺漏值(Missing Value) 6
2-1-1 K-鄰近算法(K-nearest neighbor, KNN) 11
2-1-2 鏈式方程多重插補法(Multivariate Imputation by Chained Equations, MICE) 11
2-1-3 隨機森林(Random Forest, RF) 12
2-1-4 深度神經網路(Deep Neural Network, DNN) 13
2-2 資料正規化(Normalization) 14
2-3 分類器 15
2-3-1 支持向量機(Support Vector Machine, SVM) 17
2-3-2 隨機森林(Random Forest, RF) 18
2-3-3 深度神經網路(Deep Neural Network, DNN) 18
2-3-4 深度信念網路堆疊深度神經網路(Deep Belief Network stacked Deep Neural Network, DBN-DNN) 19
2-4 資料平衡化 20
三、研究設計 21
3-1 實驗資料集 22
3-2 研究一 23
3-3 研究二 24
3-4 研究三 25
3-5 實驗參數設定、方法 25
3-6 評估指標 27
3-6-1 AUC (Area Under ROC Curve) 28
3-6-2 Type II錯誤 29
四、實驗結果與分析 30
4-1 探討補值在破產與信用領域的影響 30
4-1-1 補值在不同分類器的效果 31
4-1-2 補值在大小遺漏率下對不同分類器的影響 41
4-1-3 補值的有無對整體的影響 52
4-1-4 僅補值下不同補值方法的比較 54
4-1-5 僅補值下不同分類器的比較 57
4-1-6 小結 58
4-2 探討補值搭配正規化的影響 59
4-2-1 補值搭配正規化對不同分類器的影響 60
4-2-2 補值搭配正規化下分類器的比較 71
4-2-3 補值搭配正規化下不同補值方法的比較 74
4-2-4 小結 76
4-3 機器學習與深度學習的比較 77
4-4 探討最佳組合 83
4-4-1 各分類器下的最佳補值方法 83
4-4-2 大資料集與小資料集最佳組合 85
4-4-3 信用與破產資料集中的最佳組合 87
五、結論 89
5-1 結論與貢獻 89
5-2 未來研究方向與建議 91
參考文獻 92

參考文獻

[1] F. M. Bianchi, E. Maiorino, M. C. Kampffmeyer, A. Rizzi, and R. Jenssen, “An overview and comparative analysis of recurrent neural networks for short term load forecasting,” arXiv preprint arXiv:1705.04378, July 2017.
[2] J. Liu, F. Chao, Y.-C. Lin, and C.-M. Lin, “Stock prices prediction using deep learning models,” arXiv preprint arXiv:1909.12227, September 2019.
[3] K. Fu, D. Cheng, Y. Tu, and L. Zhang, “Credit card fraud detection using convolutional neural networks,” in Proceedings of International Conference on Neural Information Processing, 2016, pp. 483-490.
[4] M. L. Brown, and J. F. Kros, “Data mining and the impact of missing data,” Industrial Management & Data Systems, vol. 103, no. 8, pp. 611-621, November 2003.
[5] K. Lakshminarayan, S. A. Harp, and T. Samad, “Imputation of missing data in industrial databases,” Applied Intelligence, vol. 11, no. 3, pp. 259-275, November 1999.
[6] E. D. De Leeuw, J. J. Hox, and M. Huisman, “Prevention and treatment of item nonresponse,” Journal of Official Statistics, vol. 19, pp. 153-176, January 2003.
[7] S. Khemakhem, F. B. Said, and Y. Boujelbene, “Credit risk assessment for unbalanced datasets based on data mining, artificial neural network and support vector machines,” Journal of Modelling in Management, vol. 13, no. 4, pp. 932-951, November 2018.
[8] R. Florez-Lopez, “Effects of missing data in credit risk scoring. A comparative analysis of methods to achieve robustness in the absence of sufficient data,” Journal of the Operational Research Society, vol. 61, no. 3, pp. 486-501, February 2010.
[9] L. Zhou, and K. K. Lai, “Adaboost models for corporate bankruptcy prediction with missing data,” Computational Economics, vol. 50, no. 1, pp. 69-94, June 2017.
[10] A. Jadhav, D. Pramod, and K. Ramanathan, “Comparison of performance of data imputation methods for numeric dataset,” Applied Artificial Intelligence, vol. 33, no. 10, pp. 913-933, July 2019.
[11] J. Han, J. Pei, and M. Kamber, Data mining: Concepts and techniques: Elsevier, 2011.
[12] A. M. Wood, I. R. White, and S. G. Thompson, “Are missing outcome data adequately handled? A review of published randomized controlled trials in major medical journals,” Clinical trials, vol. 1, no. 4, pp. 368-376, August 2004.
[13] H. Jeličić, E. Phelps, and R. M. Lerner, “Use of missing data methods in longitudinal studies: The persistence of bad practices in developmental psychology,” Developmental psychology, vol. 45, no. 4, pp. 1195, August 2009.
[14] L. Wilkinson, “Statistical methods in psychology journals: Guidelines and explanations,” American Psychologist, vol. 54, no. 8, pp. 594-604, August 1999.
[15] M. Zhimin, P. Zhisong, H. Guyu, and Z. Luwen, “Treating missing data processing based on neural network and adaboost,” in Proceedings of 2007 IEEE International Conference on Grey Systems and Intelligent Services, 2007, pp. 1107-1111.
[16] A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the em algorithm,” Journal of the Royal Statistical Society: Series B (Methodological), vol. 39, no. 1, pp. 1-22, April 1977.
[17] A. Feelders, “Handling missing data in trees: Surrogate splits or statistical imputation?,” in Proceedings of European Conference on Principles of Data Mining and Knowledge Discovery, 1999, pp. 329-334.
[18] A. Hapfelmeier, and K. Ulm, “Variable selection by random forests using data with missing values,” Computational Statistics & Data Analysis, vol. 80, pp. 129-139, January 2014.
[19] I. Noviandi, and I. Sumitra, “Classification consumer credit for missing value dataset,” MS&E, vol. 407, no. 1, pp. 012173, May 2018.
[20] U. Garciarena, and R. Santana, “An extensive analysis of the interaction between missing data types, imputation methods, and supervised classifiers,” Expert Systems with Applications, vol. 89, pp. 52-65, December 2017.
[21] W.-C. Lin, and C.-F. Tsai, “Missing value imputation: A review and analysis of the literature (2006–2017),” Artificial Intelligence Review, vol. 53, no. 2, pp. 1487-1509, April 2020.
[22] E. Acuna, and C. Rodriguez, "The treatment of missing values and its effect on classifier accuracy," Classification, clustering, and data mining applications, pp. 639-647: Springer, 2004.
[23] R. J. Little, and D. B. Rubin, Statistical analysis with missing data: John Wiley & Sons, 2019.
[24] M. P. Jones, “Indicator and stratification methods for missing explanatory variables in multiple linear regression,” Journal of the American Statistical Association, vol. 91, no. 433, pp. 222-230, March 1996.
[25] J. M. Jerez, I. Molina, P. J. García-Laencina, E. Alba, N. Ribelles, M. Martín, and L. Franco, “Missing data imputation using statistical and machine learning methods in a real breast cancer problem,” Artificial Intelligence in Medicine, vol. 50, no. 2, pp. 105-115, October 2010.
[26] J. Poulos, and R. Valle, “Missing data imputation for supervised learning,” Applied Artificial Intelligence, vol. 32, no. 2, pp. 186-196, October 2018.
[27] L. Malan, C. M. Smuts, J. Baumgartner, and C. Ricci, “Missing data imputation via the expectation-maximization algorithm can improve principal component analysis aimed at deriving biomarker profiles and dietary patterns,” Nutrition Research, vol. 75, pp. 67-76, January 2020.
[28] D. Wang, Y. Lv, Z. Guo, X. Li, Y. Li, J. Zhu, D. Yang, J. Xu, C. Wang, and S. Rao, “Effects of replacing the unreliable cdna microarray measurements on the disease classification based on gene expression profiles and functional modules,” Bioinformatics, vol. 22, no. 23, pp. 2883-2889, January 2006.
[29] J. Tuikkala, L. L. Elo, O. S. Nevalainen, and T. Aittokallio, “Missing value imputation improves clustering and interpretation of gene expression microarray data,” BMC Bioinformatics, vol. 9, no. 1, pp. 1-14, April 2008.
[30] M. C. De Souto, P. A. Jaskowiak, and I. G. Costa, “Impact of missing data imputation methods on gene expression clustering and classification,” BMC Bioinformatics, vol. 16, no. 1, pp. 64, February 2015.
[31] H. C. Valdiviezo, and S. Van Aelst, “Tree-based prediction on incomplete data using imputation or surrogate decisions,” Information Sciences, vol. 311, pp. 163-181, August 2015.
[32] A. Hapfelmeier, T. Hothorn, and K. Ulm, “Recursive partitioning on incomplete data using surrogate decisions and multiple imputation,” Computational Statistics & Data Analysis, vol. 56, no. 6, pp. 1552-1565, June 2012.
[33] S. Ghorbani, and M. C. Desmarais, “Performance comparison of recent imputation methods for classification tasks over binary data,” Applied Artificial Intelligence, vol. 31, no. 1, pp. 1-22, March 2017.
[34] S. G. Liao, Y. Lin, D. D. Kang, D. Chandra, J. Bon, N. Kaminski, F. C. Sciurba, and G. C. Tseng, “Missing value imputation in high-dimensional phenomic data: Imputable or not, and how?,” BMC Bioinformatics, vol. 15, no. 1, pp. 346, November 2014.
[35] K. Kornelsen, and P. Coulibaly, “Comparison of interpolation, statistical, and data-driven methods for imputation of missing values in a distributed soil moisture dataset,” Journal of Hydrologic Engineering, vol. 19, no. 1, pp. 26-43, January 2014.
[36] E.-L. Silva-Ramírez, R. Pino-Mejías, and M. López-Coello, “Single imputation with multilayer perceptron and multiple imputation combining multilayer perceptron and k-nearest neighbours for monotone patterns,” Applied Soft Computing, vol. 29, pp. 65-74, April 2015.
[37] T. Cover, and P. Hart, “Nearest neighbor pattern classification. Ieee transactions on information theory,” IT-13, vol. 13, no. 1, pp. 19-17, January 1967.
[38] O. Troyanskaya, M. Cantor, G. Sherlock, P. Brown, T. Hastie, R. Tibshirani, D. Botstein, and R. B. Altman, “Missing value estimation methods for DNA microarrays,” Bioinformatics, vol. 17, no. 6, pp. 520-525, June 2001.
[39] J. L. Schafer, and J. W. Graham, “Missing data: Our view of the state of the art,” Psychological Methods, vol. 7, no. 2, pp. 147, June 2002.
[40] M. J. Azur, E. A. Stuart, C. Frangakis, and P. J. Leaf, “Multiple imputation by chained equations: What is it and how does it work?,” International Journal of Methods in Psychiatric Research, vol. 20, no. 1, pp. 40-49, February 2011.
[41] F. Tang, and H. Ishwaran, “Random forest missing data algorithms,” Statistical Analysis and Data Mining: The ASA Data Science Journal, vol. 10, no. 6, pp. 363-377, June 2017.
[42] D. J. Stekhoven, and P. Bühlmann, “Missforest—non-parametric missing value imputation for mixed-type data,” Bioinformatics, vol. 28, no. 1, pp. 112-118, October 2012.
[43] P. K. Sharpe, and R. Solly, “Dealing with missing values in neural network-based diagnostic systems,” Neural Computing & Applications, vol. 3, no. 2, pp. 73-77, June 1995.
[44] N. V. Dharwadkar, and P. S. Patil, “Customer retention and credit risk analysis using ann, svm and dnn,” International Journal of Society Systems Science, vol. 10, no. 4, pp. 316-332, October 2018.
[45] D. Liang, C.-F. Tsai, and H.-T. Wu, “The effect of feature selection on financial distress prediction,” Knowledge-Based Systems, vol. 73, pp. 289-297, December 2015.
[46] M. Wagle, Z. Yang, and Y. Benslimane, “Bankruptcy prediction using data mining techniques,” in Proceedings of 2017 8th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES), 2017, pp. 1-4.
[47] D. Singh, and B. Singh, “Investigating the impact of data normalization on classification performance,” Applied Soft Computing, pp. 105524, May 2019.
[48] L. S. Begu, M. D. Vasilescu, L. Stanila, and R. Clodnitchi, “China-angola investment model,” Sustainability, vol. 10, no. 8, pp. 2936, August 2018.
[49] Z. Zhao, S. Xu, B. H. Kang, M. M. J. Kabir, Y. Liu, and R. Wasinger, “Investigation and improvement of multi-layer perceptron neural networks for credit scoring,” Expert Systems with Applications, vol. 42, no. 7, pp. 3508-3516, May 2015.
[50] F. Barboza, H. Kimura, and E. Altman, “Machine learning models and bankruptcy prediction,” Expert Systems with Applications, vol. 83, pp. 405-417, October 2017.
[51] J. R. de Castro Vieira, F. Barboza, V. A. Sobreiro, and H. Kimura, “Machine learning models for credit analysis improvements: Predicting low-income families’ default,” Applied Soft Computing, vol. 83, pp. 105640, October 2019.
[52] S. Xuan, G. Liu, Z. Li, L. Zheng, S. Wang, and C. Jiang, “Random forest for credit card fraud detection,” in Proceedings of 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), 2018, pp. 1-6.
[53] G. Zhao, G. Zhang, Q. Ge, and X. Liu, “Research advances in fault diagnosis and prognostic based on deep learning,” in Proceedings of 2016 Prognostics and system health management conference (PHM-Chengdu), 2016, pp. 1-6.
[54] X. Zhang, J. Zhou, and W. Chen, “Data-driven fault diagnosis for pemfc systems of hybrid tram based on deep learning,” International Journal of Hydrogen Energy, pp. 13483-13495, May 2020.
[55] Y. Guo, “Credit risk assessment of p2p lending platform towards big data based on bp neural network,” Journal of Visual Communication and Image Representation, vol. 71, pp. 102730, August 2019.
[56] C. Luo, D. Wu, and D. Wu, “A deep learning approach for credit scoring using credit default swaps,” Engineering Applications of Artificial Intelligence, vol. 65, pp. 465-470, October 2017.
[57] R. Sarikaya, G. E. Hinton, and A. Deoras, “Application of deep belief networks for natural language understanding,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 22, no. 4, pp. 778-784, February 2014.
[58] Y.-P. Huang, and M.-F. Yen, “A new perspective of performance comparison among machine learning algorithms for financial distress prediction,” Applied Soft Computing, vol. 83, pp. 105663, October 2019.
[59] S. M. Erfani, S. Rajasegarar, S. Karunasekera, and C. Leckie, “High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning,” Pattern Recognition, vol. 58, pp. 121-134, October 2016.
[60] C.-C. Chang, and C.-J. Lin, “Libsvm: A library for support vector machines,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 2, no. 3, pp. 1-27, May 2011.
[61] S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, “Data mining for credit card fraud: A comparative study,” Decision Support Systems, vol. 50, no. 3, pp. 602-613, February 2011.
[62] T. G. Dietterich, “Ensemble methods in machine learning,” in Proceedings of International Workshop on Multiple Classifier Systems, 2000, pp. 1-15.
[63] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5-32, October 2001.
[64] J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks, vol. 61, pp. 85-117, January 2015.
[65] H. Hakimpoor, K. A. B. Arshad, H. H. Tat, N. Khani, and M. Rahmandoust, “Artificial neural networks’ applications in management,” World Applied Sciences Journal, vol. 14, no. 7, pp. 1008-1019, January 2011.
[66] G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Computation, vol. 18, no. 7, pp. 1527-1554, May 2006.
[67] F. Provost, “Machine learning from imbalanced data sets,” in Proceedings of Invited paper for the AAAI’2000 Workshop on Imbalanced Data Sets, 2000, pp. 1-3.
[68] G. M. Weiss, “Mining with rarity: A unifying framework,” ACM Sigkdd Explorations Newsletter, vol. 6, no. 1, pp. 7-19, June 2004.
[69] L. Zhang, and W. Wang, “A re-sampling method for class imbalance learning with credit data,” in Proceedings of 2011 International Conference of Information Technology, Computer Engineering and Management Sciences, 2011, pp. 393-397.
[70] R. Pierdicca, E. Malinverni, F. Piccinini, M. Paolanti, A. Felicetti, and P. Zingaretti, “Deep convolutional neural network for automatic detection of damaged photovoltaic cells,” International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, vol. 42, no. 2, pp. 893-900, May 2018.
[71] S. Vellamcheti, and P. Singh, “Class imbalance deep learning for bankruptcy prediction,” in Proceedings of 2020 First International Conference on Power, Control and Computing Technologies (ICPC2T), 2020, pp. 421-425.
[72] T. Fawcett, “An introduction to roc analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861-874, June 2006.

指導教授

蘇坤良(Kuen-Liang Sue)

審核日期

2020-7-29

推文