資料載入中.....
|
請使用永久網址來引用或連結此文件:
https://ir.lib.ncu.edu.tw/handle/987654321/106206
|
| 題名: | A comparative study of classifier ensembles for bankruptcy prediction |
| 作者: | 蔡志豐;Tsai, Chih-Fong;Hsu, Yu-Feng;Yen, David C. |
| 貢獻者: | 管理學院資訊管理學系 |
| 關鍵詞: | Bankruptcy prediction;Classifier ensembles;Credit scoring;Data mining;Machine learning |
| 日期: | 2014-01-01 |
| 上傳時間: | 2026-04-23 13:13:16 (UTC+8) |
| 出版者: | Elsevier BV;Elsevier B.V |
| 摘要: | 摘要： •This paper examines the construction issues of classifier ensembles for bankruptcy prediction.•The first issue focuses on the classification techniques, which are based on MLP, SVM, and DT.•The second issue is the combination method, which is based on bagging and boosting.•The third issue is based on examining different numbers of combined classifiers.•We show that DT ensembles composed of 80–100 classifiers using the boosting method perform best. The aim of bankruptcy prediction in the areas of data mining and machine learning is to develop an effective model which can provide the higher prediction accuracy. In the prior literature, various classification techniques have been developed and studied, in/with which classifier ensembles by combining multiple classifiers approach have shown their outperformance over many single classifiers. However, in terms of constructing classifier ensembles, there are three critical issues which can affect their performance. The first one is the classification technique actually used/adopted, and the other two are the combination method to combine multiple classifiers and the number of classifiers to be combined, respectively. Since there are limited, relevant studies examining these aforementioned disuses, this paper conducts a comprehensive study of comparing classifier ensembles by three widely used classification techniques including multilayer perceptron (MLP) neural networks, support vector machines (SVM), and decision trees (DT) based on two well-known combination methods including bagging and boosting and different numbers of combined classifiers. Our experimental results by three public datasets show that DT ensembles composed of 80–100 classifiers using the boosting method perform best. The Wilcoxon signed ranked test also demonstrates that DT ensembles by boosting perform significantly different from the other classifier ensembles. Moreover, a further study over a real-world case by a Taiwan bankruptcy dataset was conducted, which also demonstrates the superiority of DT ensembles by boosting over the others.
出版者： Elsevier B.V
出版日期： 2014-11-01
出處： Applied Soft Computing, 2014-11, Vol.24, p.977-984
版權： 2014 Elsevier B.V.
識別號： ISSN: 1568-4946
識別號： EISSN: 1872-9681
識別號： DOI: 10.1016/j.asoc.2014.08.047 |
| 顯示於類別: | [資訊管理學系] 期刊論文
|
文件中的檔案:
| 檔案 |
描述 |
大小 | 格式 | 瀏覽次數 |
|---|
| index.html | | 0Kb | HTML | 13 | 檢視/開啟 |
|
在NCUIR中所有的資料項目都受到原著作權保護.
|
