Risk prediction of product-harm events using rough sets and multiple classifier fusion: an experimental study of listed companies in China

Delu Wang*, Jianping Zheng, Gang Ma, Xuefeng Song, Yun Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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).

Original languageEnglish
Pages (from-to)254-274
Number of pages21
JournalExpert Systems
Volume33
Issue number3
DOIs
Publication statusPublished - Jun 2016
Externally publishedYes

Keywords

  • product‐harm
  • risk prediction
  • multiple classifiers
  • self-organising data mining
  • rough set
  • product-harm
  • multiple classifiers

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