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

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Research and development project risk assessment using a belief rule-based system with random subspaces. / Yang, Ying; Wang, Jun; Wang, Gang; Chen, Yu Wang.

In: Knowledge-Based Systems, 2019.

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@article{33f8ffca99ef418fa241f3f93dff1639,
title = "Research and development project risk assessment using a belief rule-based system with random subspaces",
abstract = "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.",
keywords = "Belief rule-based systems, Evidential reasoning rule, Project risk assessment, Random subspaces",
author = "Ying Yang and Jun Wang and Gang Wang and Chen, {Yu Wang}",
year = "2019",
doi = "10.1016/j.knosys.2019.04.017",
language = "English",
journal = "Knowledge Based Systems",
issn = "0950-7051",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Research and development project risk assessment using a belief rule-based system with random subspaces

AU - Yang, Ying

AU - Wang, Jun

AU - Wang, Gang

AU - Chen, Yu Wang

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Belief rule-based systems

KW - Evidential reasoning rule

KW - Project risk assessment

KW - Random subspaces

U2 - 10.1016/j.knosys.2019.04.017

DO - 10.1016/j.knosys.2019.04.017

M3 - Article

AN - SCOPUS:85064846938

JO - Knowledge Based Systems

JF - Knowledge Based Systems

SN - 0950-7051

ER -