Hybrid Kernelized Fuzzy Clustering and Multiple Attributes Decision Analysis for Corporate Risk Management

文化大學機構典藏 CCUR > 商學院 > 會計學系暨研究所 > 期刊論文 > Item 987654321/36122

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://irlib.pccu.edu.tw/handle/987654321/36122

题名:	Hybrid Kernelized Fuzzy Clustering and Multiple Attributes Decision Analysis for Corporate Risk Management
作者:	Lin, Sin-Jin
贡献者:	會計系
关键词:	Decision making Risk exposure Clustering Multiple attributes decision analysis
日期:	2017-06
上传时间:	2017-06-08
摘要:	This study introduces an emerging risk management architecture by extending balanced scorecards (BSC) with risk exposure considerations for corporate operating performance assessment and then constructs a hybrid mechanism that combines kernelized fuzzy C-means (KFCM), multiple attributes decision analysis (MADA), and extreme learning machine (ELM) for corporate operating performance forecasting. KFCM is implemented to do the clustering task for each corporate under each aspect of BSC. No specific corporate reaches optimal performance under each assessing measure-that is, dissimilar assessing criteria leads to dissimilar outcomes. This method can be transformed into a MADA task and a MADA algorithm that can yield a reliable outcome systematically. Sequentially, the outcome is fed into ELM to construct the performance forecasting mechanism. The introduced mechanism with outstanding forecasting performance comes with a critical challenge: it lacks interpretability, which impedes its real-life usage. To cope with this problem, the rough set theory (RST) is employed to extract the inherent decision logics from the black-box model and visualize it in human readable formats. The introduced model has been examined by real cases and is a promising alternative for corporate risk management.
關聯:	INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 卷: 19 期: 3 頁碼: 659-670
显示于类别:	[會計學系暨研究所 ] 期刊論文

文件中的档案:

档案	描述	大小	格式	浏览次数
index.html		0Kb	HTML	326	检视/开启

检视Licence

在CCUR中所有的数据项都受到原著作权保护.

数据加载中.....