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Risk prediction of product-harm events using rough sets and multiple classifier fusion: an experimental study of listed companies in China.
- Source :
-
Expert Systems . Jun2016, Vol. 33 Issue 3, p254-274. 21p. - Publication Year :
- 2016
-
Abstract
- With the increasing of frequency and destructiveness of product-harm events, study on enterprise crisis management becomes essentially important, but little literature thoroughly explores the risk prediction method of product-harm event. In this study, an initial index system for risk prediction was built based on the analysis of the key drivers of the product-harm event's evolution; ultimately, nine risk-forecasting indexes were obtained using rough set attribute reduction. With the four indexes of cumulative abnormal returns as the input, fuzzy clustering was used to classify the risk level of a product-harm event into four grades. In order to control the uncertainty and instability of single classifiers in risk prediction, multiple classifier fusion was introduced and combined with self-organising data mining (SODM). Further, an SODM-based multiple classifier fusion (SB-MCF) model was presented for the risk prediction related to a product-harm event. The experimental results based on 165 Chinese listed companies indicated that the SB-MCF model improved the average predictive accuracy and reduced variation degree simultaneously. The statistical analysis demonstrated that the SB-MCF model significantly outperformed six widely used single classification models (e.g. neural networks, support vector machine, and case-based reasoning) and other six commonly used multiple classifier fusion methods (e.g. majority voting, Bayesian method, and genetic algorithm). [ABSTRACT FROM AUTHOR]
- Subjects :
- *RISK assessment
*PRODUCT recall
*SELF-organizing systems
*DATA mining
*ROUGH sets
Subjects
Details
- Language :
- English
- ISSN :
- 02664720
- Volume :
- 33
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Expert Systems
- Publication Type :
- Academic Journal
- Accession number :
- 116146338
- Full Text :
- https://doi.org/10.1111/exsy.12148