Research and development project risk assessment using a belief rule-based system with random subspaces

Research and development (R&D) project risk assessment mainly focuses on predicting the likelihood of project success and effectively controlling risks. The belief rule-based (BRB) inference method has been applied for risk assessment, due to its strong interpretability and high prediction accuracy. However, lots of risk factors related to R&D projects will lead to an oversized rule base when the standard BRB method is used to evaluate project performance. In this research, a novel predictive evaluation framework is proposed to address this issue, where a RS-BRB model, namely the BRB with random subspaces, is developed to assess R&D project risks in a modular way. Firstly, multiple subspaces with low dimensions are constructed by random sampling. Subsequently, a BRB subsystem is developed as a base learner in each subspace to obtain a prediction result, and the evidential reasoning rule is adopted to combine the prediction results from different BRB subsystems. The proposed model was validated using the data from R&D projects in Chinese industries. Comparative analysis results show that the proposed model has superior prediction accuracy and can overcome the problem of combinational explosions without information loss. (C) 2019 Elsevier B.V. All rights reserved.