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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 曾惠斌(Hui-Ping Tserng) | |
| dc.contributor.author | Minh Tran | en |
| dc.contributor.author | 陳明 | zh_TW |
| dc.date.accessioned | 2021-06-15T06:42:40Z | - |
| dc.date.available | 2013-07-18 | |
| dc.date.copyright | 2011-07-18 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-07-08 | |
| dc.identifier.citation | 1. Altman, E (1968). “Financial Ratios, Discriminant analysis and the prediction of corporate bankruptcy”, Journal of Finance. pp. 589-609.
2. Abidali, A.,& Harris, F (1995). “A methodology for predicting company failure in the construction industry”. Construction Management and Economics. pp. 189-196. 3. Smith, Kate (2002). “Neural Networks for Business: An Introduction. Neural Networks in Business” , Idea Group Publishing. 4. Beaver,W (1966).“ Financial ratio as predictors of failure”. Journal of Accounting Research. pp 71-111. 5. Chava,S., & Jarrow, R (2004). “Bankruptcy prediction with industry effects”. Review of Finance. pp. 537-569. 6. Chawla, N., Bowyer, K., Hall, L.,& Kegelmeyer,P (2002). “SMOTE: Synthetic Minority Over sampling Technique”. International Conference on Knowledge Based Computer Systems 7. Coats,P.,& Fant, L (1993). “Recognizing financial distress pattern using a neural-network tool”. Financial Management. pp. 142-152 8. Hsu,KL., Gupta,HV.,Gao X.,& Sorooshian, S (1999). “Estimation of physical variables from multichannel remotely sensed imagery using a neural network: application to rainfall estimation”. Water Resources Research. pp. 1606–1618. 9. Kangari, R., Fraid, F.,& Elgharib, H (1992). “Finanical performance analysis for construction industry”, Journal of Construction Engineering and Management .pp. 349-361 10. Langford, D., Iyaga, R.,& Komba, D (1993). “Prediction of solvency in construction companies”, Construction Management and Economics . pp. 317-325. 11. Lin, Gwo-Fong.,& Chen, Lu-Hsien (2005). “Application of an artificial neural network to typhoon rainfall forecasting Hydrological Processes”. Volume 19, Issue 9. pp. 1825–1837 12. Lee, K., Booth, D., & Alam, P (2004). Back propagation and Kohonen Self-Organizing Feature Map in Bankruptcy Prediction. Neural Networks in Business Forecasting. G.Peter Zhang, Idea Group Publishing. pp. 158-171 13. Lippmann, R (1987). “An Introduction to Computing with Neural Networks”. IEEE ASSP Magazine, vol. 4, no. 2. pp. 4–22. 14. Kishan, M (1997). Elements of Artificial Neural Network. Cambridge, Mass: MIT Press. 15. Lee , Ming-Chang.,& To, Chang (2010). “Comparison of Support Vector Machine and Back Propagation Neural Network in Evaluating the Enterprise Financial Distress”. International Journal of Artificial Intelligence & Applications (IJAIA), Vol.1, No.3. 16. Mason, R.,& Harris, F (1979). “Predicting company failure in the construction industry”. Proceedings Institution of Civil Engineers. pp. 301-307. 17. Odom, M.,& Sharda, R (1990). “A neural network model for bankruptcy prediction”. IJCNN International Joint Conference. pp.163-168 18. Ohlson, J (1980). “Financial ratios and the probabilistic prediction of bankruptcy”. Jouranl of Accounting Research. pp. 109-131. 19. Russell, J.,& Zhai, H (1996). “Prediction contractor failure using stochastic dynamics of economic and financial variables”. Construction Engineering and Management. pp. 183-191. 20. Tserng, H.Ping., Lin, Gwo-Fong., Tsai, L.Ken, Chen, Po-Cheng.” An Enforced Support Vector Machine Model for Construction Default Prediction”. Journal of Construction Engineering and Management. 21. Zhang, G., Hu, M., Patuwo, B.,& Indro, D (1999). “Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis”, European Journal of Operational Research. pp. 16-32 22. Zuradak, J (1992). Introduction to Artificial Neural Systems. St. Paul, West 23. Zmijewski, M (1984). Methodological issues related to the estimation of financial distress prediction model. Journal of Accounting Research. pp. 59-82 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47894 | - |
| dc.description.abstract | Construction industry plays a major part in any nation economy. However, the construction industry tends to face high risk due to the particular characteristic of the environment and high competition. Therefore, many researches have been conducted to find an appropriate model to forecast bankruptcy in construction sector. Artificial Neural Network (ANN) using Back Propagation Algorithm has been applied in this area since the early 1990s, and has been showed the promising outcome. Accordingly, in this study Back Propagation Network (BPN) was selected to construct a model in bankruptcy prediction for construction industry. In the previous study employing ANN methods, the sample-matching technique was usually used, which lead to sample selection biases, likely due to ANN’s inability to tackle between-class imbalance problem. In this research Back Propagation Network (BPN) using over-sampling techniques with all available firm-year data was proposed so as to tackle between-class imbalance challenge. The two over-sampling techniques used were: Enforce training and Synthetic Minority Over-Sampling TEchnique (SMOTE). The empirical result of this study showed that the BPN using SMOTE was out performed the BPN original and EBPN. Accordingly, BPN using SMOTE are suggested as an alternative to the existing model | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T06:42:40Z (GMT). No. of bitstreams: 1 ntu-100-R98521746-1.pdf: 5889365 bytes, checksum: dd9c5ce465cf8ffc9f7f639bf47626b5 (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | TABLE OF CONTENTS
ACKNOWLEDGEMENT ii ABSTRACT iii LIST OF FIGURES vi LIST OF TABLES vii CHAPTER 1 INTRODUCTION 1 1.1. Introduction 1 1.2. Motivation of Thesis 2 1.3. Problem Statement 3 1.4. Research Objective 4 1.5. Research Scope and Limitation 4 1.6. Thesis Structure 5 CHAPTER 2 LITERATURE REVIEW 6 2.1 Non-related construction bankruptcy prediction model 6 2.2 Bankruptcy Prediction research in Construction industry 10 2.3 Application of ANNS in bankruptcy prediction 13 2.4 Summary 14 CHAPTER 3 RESEARCH METHODLOGY 15 3.1 Back propagation network 15 3.2 Model design 16 3.3 Over-sampling techniques 21 3.3.1. Between-class imbalance problem in data set 21 3.3.2. Enforced training procedure 22 3.3.3. Synthetic Over-sampling TEchnique (SMOTE) 22 3.4 Discriminatory power 24 3.5 Validation process 26 3.6 Data collection 27 3.6.1. Data collection 27 3.6.2. Data collection principle 28 3.6.3. Input variable selection 29 3.7 Summary 32 CHAPTER 4 RESULTS AND DISCUSSION 33 4.1 Validation result 33 4.1.1 Selection of accurate model 33 4.1.2. Over-sampling technique validation result 35 4.2. Discussion 37 4.3. Summary 39 CHAPTER 5 CONCLUSIONS 40 REFERENCES 42 APPENDICES 45 A1. Total Samples of construction companies 45 A. 2. ROC and average AUC value (Enforce training) 51 A.3. ROC and average AUC value (SMOTE) 63 | |
| dc.language.iso | en | |
| dc.title | 重覆取樣BPN模型應用於營建公司財務危機預測之研究 | zh_TW |
| dc.title | A Back Propagation Neural Network using Over-sampling techniques in bankruptcy prediction in construction industry | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 郭斯傑(Sy-Jye Guo),陳柏翰(Po-Han Chen) | |
| dc.subject.keyword | 違約概率預測,建築業,人工神經網絡,反向傳播 算法,類間的不平衡,強迫訓練,合成少數股東採樣技術, | zh_TW |
| dc.subject.keyword | default probability prediction,construction industry,Artificial Neural Network,Back Propagation Algorithm,between-class imbalance,Enforced training,Synthetic Minority Over-Sampling TEchnique, | en |
| dc.relation.page | 74 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-07-08 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
| 顯示於系所單位： | 土木工程學系 | |
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