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| 研究生： |
林芳婷 Lisayuri - Limanto |
|---|---|
| 論文名稱： |
A Hybrid Inference Model Based on Synthetic Minority Over-sampling Technique and Evolutionary Least Square SVM for Predicting Construction Contractor Default Status A Hybrid Inference Model Based on Synthetic Minority Over-sampling Technique and Evolutionary Least Square SVM for Predicting Construction Contractor Default Status |
| 指導教授： |
鄭明淵
Min-Yuan Cheng |
| 口試委員: |
姚乃嘉
Nie-Jia Yau 晁立中 Chih-Chao Chung 周瑞生 Jui-Sheng Chou |
| 學位類別： |
碩士 Master |
| 系所名稱： |
工程學院 - 營建工程系 Department of Civil and Construction Engineering |
| 論文出版年： | 2013 |
| 畢業學年度： | 101 |
| 語文別： | 英文 |
| 論文頁數： | 118 |
| 中文關鍵詞： | Contractor Default Status Prediction 、Synthetic Minority Over-sampling Technique 、Least Squares Support Vector Machine 、Differential Evolution 、Synthetic 、Contractor Default Status Prediction |
| 外文關鍵詞： | Differential Evolution, Least Squares Support Vector Machine, Synthetic Minority Over-sampling Technique, Contractor Default Status Prediction, Synthetic, Contractor Default Status Prediction |
| 相關次數： | 點閱：286 下載：3 |
| 分享至: |
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Construction industry has several typical characteristics that are different compared to other economy sectors, including the dependability among project stakeholders. Thus, financial status of a construction company is an important issue in the construction industry. Assessing the financial status is challenging and the mapping relationship of input factors and the financial status of a company is very complicated. To avoid biased result and represent company’s financial condition, all available construction firm-years data in verified database center is employed in this study which caused imbalanced issue. This paper presents a hybrid inference model based on the financial ratios to estimate the contractor financial performance. The proposed model is constructed by combining Synthetic Minority Over-sampling Technique (SMOTE), Least Squares Support Vector Machine (LS-SVM), and Differential Evolution (DE). In the new model, SMOTE acts imbalanced dataset problem handling of non-default and default samples, LS-SVM is used as a supervised machine learning technique for classification, and DE is employed for specifying the optimal parameter of LS-SVM. A total record of 1695 construction contractor firm-years observations from 84 non-defaulted and 28 defaulted companies is collected and used to train and validate the proposed model. The Area Under the Curve (AUC) is utilized as the performance measurement of prediction results. As shown in the experimental results, the proposed models (AUC=0.98463) outperformed the other benchmark models, including Evolutionary Support Vector Machine Inference Model (ESIM), Support Vector Machine (SVM), Artificial Neural Network (ANN), and logistic regression. Therefore, the proposed approach is a promising alternative for predicting contractor default status.
Construction industry has several typical characteristics that are different compared to other economy sectors, including the dependability among project stakeholders. Thus, financial status of a construction company is an important issue in the construction industry. Assessing the financial status is challenging and the mapping relationship of input factors and the financial status of a company is very complicated. To avoid biased result and represent company’s financial condition, all available construction firm-years data in verified database center is employed in this study which caused imbalanced issue. This paper presents a hybrid inference model based on the financial ratios to estimate the contractor financial performance. The proposed model is constructed by combining Synthetic Minority Over-sampling Technique (SMOTE), Least Squares Support Vector Machine (LS-SVM), and Differential Evolution (DE). In the new model, SMOTE acts imbalanced dataset problem handling of non-default and default samples, LS-SVM is used as a supervised machine learning technique for classification, and DE is employed for specifying the optimal parameter of LS-SVM. A total record of 1695 construction contractor firm-years observations from 84 non-defaulted and 28 defaulted companies is collected and used to train and validate the proposed model. The Area Under the Curve (AUC) is utilized as the performance measurement of prediction results. As shown in the experimental results, the proposed models (AUC=0.98463) outperformed the other benchmark models, including Evolutionary Support Vector Machine Inference Model (ESIM), Support Vector Machine (SVM), Artificial Neural Network (ANN), and logistic regression. Therefore, the proposed approach is a promising alternative for predicting contractor default status.
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