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研究生:張懷倫
研究生(外文):Huai-lun Chang
論文名稱:結合領域知識與機器運算之新的特徵選取方法:應用於財務危機預警預測之問題
論文名稱(外文):Novel feature selection methods to Financial Distressed Prediction problem
指導教授:梁德容梁德容引用關係
指導教授(外文):De-ron Liang
學位類別:碩士
校院名稱:國立中央大學
系所名稱:軟體工程研究所
學門:社會及行為科學學門
學類:經濟學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:83
中文關鍵詞:遺傳演算法財務危機預測特徵選取
外文關鍵詞:genetic algorithmwrapper methodFinancial Distressed PredictionFeature Selection
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在目前眾多的研究議題中,特徵選取(Variable and feature selection)已經是一個越來越令人關注的議題。尤其是當我們收集樣本的特徵集(Feature sets)成千上百的增加的時候,一個好的特徵選取方法可以使得結果令人滿意。
本論文提出了一個概念,此概念是嘗詴去結合專家意見(Expert recommendation)與機器學習演算法(Machine Learning Algorithm)後,創造出一種混合型的特徵選取方法(Novel feature selection methods),並且使用預測財務危機公司(Financial Distressed Prediction,簡稱FDP)此問題當作案例做為實驗證實。
本論文的貢獻在於對於特徵選取這個議題而言,我們提供了兩個新的方法:Advanced wrapper method & Mix of Expert and Machine(MEM)。而這兩個方法對於應用在非結構化的商業問題上(unstructured nature of the business problems)有著比貣以往的方法更佳的結果-擁有更勝於以往的預測準確率以及為數少量的推薦特徵集。
Variable and feature selection is an important issue in plenty of issues, especially feature sets is growing up violently. A good variable and feature selection will have bearing on performance of result.
In this paper, we apply a new concept that combines expert recommendation and machine learning algorithm to create a novel feature selection, and utilize the financial distress prediction problem as a study case to prove our idea.
We apply two methods that Advanced wrapper method & mixed of expert and machine (MEM) to applicate in nonstructed business problem and believe this proposed methods be better performance than original methods included predictor accuracy and few feature set.
摘要................................................................................................................................ I
Abstract ......................................................................................................................... II
致謝.............................................................................................................................. III
目錄............................................................................................................................ VII
圖目錄.......................................................................................................................... IX
表目錄........................................................................................................................... X
1 緒論........................................................................................................................ 1
1.1. 研究背景.................................................................................................... 1
1.2. 研究動機.................................................................................................... 3
1.3. 問題定義(Problem Definition) .................................................................. 8
2. 文獻探討.............................................................................................................. 10
2.1. Feature Selection相關文獻探討 ............................................................. 10
3. Advanced wrapper method .................................................................................. 13
3.1. Advanced wrapper method演算法概念與流程。 ................................. 13
3.2. 實驗A. 前提假設、實驗組與對照組 ................................................... 17
3.3. 實驗A. 結果分析 ................................................................................... 20
4. Mixed of Expert and Machine (MEM) ................................................................ 23
4.1. MEM演算法概念與流程 ....................................................................... 23
4.2. 實驗B. 前提假設、實驗組與對照組 ................................................... 33
4.3. 實驗B. 結果分析 ................................................................................... 36
5. 結論...................................................................................................................... 39
6. 未來展望.............................................................................................................. 40
7. 參考文獻.............................................................................................................. 42
8. 附錄...................................................................................................................... 46
8.1. 附錄A 實驗公司樣本 ............................................................................ 46
8.2. 附錄B. 初始特徵集 ............................................................................... 53
8.3. 附錄C. 著名論文所推薦之特徵集集合 ............................................. 64
8.4. 附錄D Feature set by expert clustering .................................................. 66
8.5. 附錄E. MEM_Ttest在不同階段所提供之最終推薦特徵集................ 70
8.6. 附錄F. 實驗B.詳細數據(包含準確率、Type I Error) ........................ 71
[1] W. H. Beaver, "Financial Ratios As Predictors of Failure," Journal of Accounting Research, vol. 4, pp. 77-111, 1966.
[2] E. I. Altman, "Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy," The Journal of Finance, vol. Vol. 23, pp. pp. 589-609, 1968.
[3] M. E. Zmijewski, "Methodological Issues Related to the Estimation of Financial Distress Prediction Models," Journal of Accounting Research, vol. 22, pp. pp. 59-82, 1984.
[4] C.-H. Wu, et al., "A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy," Expert Systems with Applications, vol. 32, pp. 397-408, 2007.
[5] K. Y. Tam and M. Y. Kiang, "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, vol. 38, pp. pp. 926-947, 1992.
[6] J. Sun and H. Li*, "Financial distress early warning based on group dicision making," Computers & operations research, vol. 36, pp. 885-906, 2007.
[7] K.-S. Shin, et al., "An application of support vector machines in bankruptcy prediction model," Expert Systems with Applications, vol. 28, pp. Pages 127-135, 2005.
[8] E. N. Ozkan-Gunay and M. Ozkan, "Prediction of bank failures in emerging financial markets: an ANN approach," Journal of Risk Finance, vol. 8, pp. pp.465 - 480, 2007.
[9] J. A. Ohlson, "Financial Ratios and the Probabilistic Prediction of Bankruptcy," Journal of Accounting Research, vol. 18, pp. pp. 109-131, 1980.
[10] H. Li* and J. Sun, "Business failure prediction using hybrid2 case-based reasoning(H2CBR)," Computers & Operations Research, vol. 37, pp. 137-151, 2010.
[11] H. Li* and J. Sun, "Gaussian case-based reasoning for business failure prediction with empirical data in China," Information Sciences, vol. 179, pp. 89-108, 2009.
[12] H. Li and J. Sun, "Ranking-order case-based reasoning for financial distress prediction," Knowledge-Based Systems, vol. 21, pp. 868-878, 2008.
[13] Z. Hua, et al., "Predicting corporate financial distress based on integration of support vector machine and logistic regression," Expert Systems with Applications, vol. 33, pp. Pages 434-440, 2007.
43
[14] Y. Ding, et al., "Forecasting financial condition of Chinese listed companies based on support vector machine," Expert Systems with Applications, vol. 34, pp. 3081-3089, 2008.
[15] E. B. Deakin, "A Discriminant Analysis of Predictors of Business Failure," Journal of Accounting Research, vol. 10, pp. 167-179, 1972.
[16] D. K. Chandra, et al., "Failure prediction of dotcom companies using hybrid intelligent techniques," Expert Systems with Applications, vol. 36, pp. 4830-4837, 2009.
[17] J. M. Carson and R. E. Hoyt, "Life Insurer Financial Distress: Classification Models and Empirical Evidence," The Joutrnal of Risk and Insuranc, vol. 62, pp. 764-775, 1995.
[18] M. Blum, "Failing Company Discriminant Analysis," Journal of Accounting Research, vol. 12, 1974.
[19] H. Ashbaugh-Skaife, et al., "The effects of corporate governance on firms’ credit ratings," Journal of Accounting and Economics, vol. 42, pp. 203-243, 2006.
[20] C.-F. Tsai and J.-W. Wu, "Using neural network ensembles for bankruptcy prediction and credit scoring," Expert Systems with Applications, vol. 34, 2008.
[21] B.-B. M, Pattern recognition and reduction of dimensionality vol. 1, 1982.
[22] S. Cho, et al., "An integrative model with subject weight based on neural network learning for bankruptcy prediction," Expert Systems with Applications, vol. 36, pp. PP 403-410., 2009.
[23] L.-H. Chen and H.-D. Hsiao, "Feature selection to diagnose a business crisis by using a real GA-based support vector machine: An empirical study," Expert Systems with Applications, vol. 35, pp. 1145-1155, 2008.
[24] H. Jo and I. Han, "Integration of case-based forecasting, neural network, and discriminate analysis for bankruptcy prediction," Expert Systems with Applications, 1996.
[25] I. Guyon and A. Elisseeff, "An Introduction to Variable and Feature Selection," The Journal of Machine Learning Research, vol. 3, 2003.
[26] G. R. I. and H. N., Analysis of variance. Newbury Park: Sage Publications, 1987.
[27] K. D. and M. K., Logistic regression: A self-learning text. New York: Springer, 1994.
[28] W. R. Klecka, Discriminant Analysis: SAGE Publications, 1980.
[29] R. Kohavi and G. H. John, "Wrappers for feature subset selection," Artificial Intelligence, vol. 97, pp. 273-324, 1997.
44
[30] A. L. Blum and P. Langley, "Selection of relevant features and examples in machine learning," Artificial Intelligence, vol. 97, pp. 245-271, 1997.
[31] Y. Saeys, et al., "A review of feature selection techniques in bioinformatics," Bioinformatics, vol. 23, pp. 2507-2517, 2007.
[32] H. Liu and L. Yu, "Toward integrating feature selection algorithms for classification and clustering," IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, vol. 17, pp. 491 - 502, April 2005.
[33] S. A. Cook, "The complexity of theorem-proving procedures," in STOC ''71 Proceedings of the third annual ACM symposium on Theory of computing, New York, NY, USA, 1971.
[34] 杜榮瑞, et al., 會計學, 2009.
[35] C. H. Gibson, Financial statement analysis : using financial accounting information, 1939.
[36] M. Dash, et al., "Feature Selection for Clustering – A Filter Solution," presented at the Second IEEE International Conference on Data Mining (ICDM''02), Maebashi City, Japan, 2002.
[37] Learning from Data: Artificial Intelligence and Statistics V (Lecture Notes in Statistics) [Paperback]: Springer; 1 edition, 1996.
[38] G. H. John, et al., "Irrelevant Features and the Subset Selection Problem," in Machine Learning: Proceedings of the Eleventh International Conference, San Francisco, CA., 1994, pp. 121-129.
[39] J. Weston, et al., "Use of the zero norm with linear models and kernel methods," J. Mach. Learn. Res., vol. 3, pp. 1439-1461, 2003.
[40] I. Guyon, et al., "Gene Selection for Cancer Classification using Support Vector Machines," Machine Learning, vol. 46, pp. 389-422, 2002.
[41] N. A. Barricelli, "Esempi numerici di processi di evoluzione," Methodos, pp. 45-68., 1954.
[42] A. Fraser, "Simulation of Genetic Systems by Automatic Digital Computers VI. Epistasis," Australian Journal of Biological Sciences, vol. 10, pp. 484-491., 1957.
[43] A. Fraser and D. BURNELL, Computer models in genetics. New York: McGraw-Hill., 1970.
[44] J. H. Holland, Adaptation in Natural and Artificial Systems, 1975.
[45] J. Markoff. (1990-08-29, 08-09.). What''s the Best Answer? It''s Survival of the Fittest.
[46] 公開觀測資訊站. Available: http://mops.twse.com.tw/mops/web/index
[47] 台灣經濟新報. Available: http://www.tej.com.tw/twsite/
[48] C.-C. Chang and C.-J. Lin. Available:
45
http://www.csie.ntu.edu.tw/~cjlin/libsvm/
[49] D. E. Goldberg and K. Deb, "A comparative analysis of selection schemes used in genetic algorithms," Foundations of genetic algorithms, 1991.
[50] M. Srinivas and L. M. Patnaik, "Genetic algorithms: a survey," Computer, vol. 27, pp. 17-26, 1994
[51] D. S. Weile and E. Michielssen, "Genetic algorithm optimization applied to electromagnetics: a review," IEEE Transactions on Antennas and Propagation, vol. 45, pp. 343-353, 1997.
[52] Z. Huang, et al., "Credit rating analysis with support vector machines and neural networks: a market comparative study," Decision Support Systems, vol. 37, pp. 543-558, 2004.
[53] F.-M. Tseng and L. Lin, "A quadratic interval logit model for forecasting bankruptcy," Omega, vol. 33, pp. 85-91, 2005.
[54] M.-J. Kim, et al., "An evolutionary approach to the combination of multiple classifiers to predict a stock price index," vol. 31, 2006.
[55] J. Sun and H. Li, "Financial Distress Prediction Based on Serial Combination of Multiple Cclassifiers," Expert Systems with Applications, vol. 36, pp. 8659-8666, 2009.
[56] J. Bi, et al., "Dimensionality Reduction via Sparse Support Vector Machines," The Journal of Machine Learning Research, vol. 3, pp. 1229-1243, 3/1 2003.
[57] M. A. Weiss, Data Structures and Algorithm Analysis in C++ (3rd Edition): Addison Wesley, 2006.
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