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

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 classifiers fusion was introduced and combined with self-organising data mining (SODM). Further, an SODM-based multiple classifiers 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 classifiers fusion methods (e.g. majority voting, Bayesian method, and genetic algorithm).Keywords product-harm; risk prediction; multiple classifiers; self-organizing data mining; rough set IntroductionProduct-harm crises can be defined as 'discrete, well publicized occurrences wherein products are found to be defective or dangerous ' (Dawar & Pillutla, 2000). The increasing complexity of products, the increased demands of customers, and the greater vigilance on the part of the media have made product-harm crises increasingly visible occurrences (Klein & Dawar, 2004). In America, the frequency of product-harm events in the Although the probability of product-harm events is relatively low for an individual enterprise, such an event, if mishandled, will not only hurt the psychology and physiology of consumers but also lead to huge economic losses and a trust crisis for the enterprise. Further, it might have undesirable social and political impacts. For instance, in an investigation conducted by Sina.com, 84% of the interviewees stated that they would no longer buy Shuanghui meat products after the 'clenbuterol' scandal, while 7.8% of the interviewees were undecided. Only 8.4% of the interviewees stated that they would continue to buy Shuanghui products, which would lead to cumulative economic losses of over 20 billion yuan for the Shuanghui group. Similarly, because of the 'melamine scandal', Sanlu Group, China's third largest dairy company, went bankrupt. Moreover, this event led to public distrust and insecurity about food safety. According to a recent market report released by AC Nielsen about China's infant milk powder, China's brand o...

Rough‐fuzzy quadratic minimum spanning tree problem

“…Therefore, in order to process and represent the useful data hidden within the imprecise or ill‐defined information, many researchers have proposed several improved theories such as probability theory, fuzzy set (Zadeh, ), rough set (Pawlak, ), and uncertainty theory (Liu, ). Several applications (Feng, Wang, & Li, ; Kar, Kar, Guo, Li, & Majumder, ; Kar, Majumder, Kar, & Pal, ; Majumder, Kundu, Kar, & Pal, ; Rani & Garg, ; Roy, Chatterjee, Bandyopadhyay, & Kar, ; Sun, Ma, & Gong, ; Wang, Zheng, Ma, Song, & Liu, ; Zhang, Xu, Zhang, & Li, ; Zhou, Lü, Liu, & Ren, ) related to those theories are also observed in the literature.…”