The Experts below are selected from a list of 27747 Experts worldwide ranked by ideXlab platform
Jian-bo Yang - One of the best experts on this subject based on the ideXlab platform.
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evidential Reasoning Rule based decision support system for predicting icu admission and in hospital death of trauma
IEEE Transactions on Systems Man and Cybernetics, 2020Co-Authors: Guilan Kong, Jian-bo Yang, Tianbing Wang, Baoguo JiangAbstract:We propose to employ evidential Reasoning (ER) Rule to construct a clinical decision support system (CDSS) to aid physicians to predict the probability of intensive care unit (ICU) admission and in-hospital death for trauma patients once they arrive at a hospital. A generalized Bayesian Rule is used to mine evidence from historical data. Evidence is profiled using a format of belief distribution, where the belief degrees of different trauma outcomes are assigned with derived probabilities linked to the corresponding outcomes. Inputs to the CDSS are clinical data of a patient, and output from the system is predicted belief degree of severe trauma, including ICU admission and in-hospital death. The inner logic of the CDSS is that pieces of evidence that match the clinical data of a patient are identified from the evidence base first, and then the ER Rule-based evidence aggregation mechanism is utilized to combine the matched evidences to arrive at a prediction. The reliability, weight, and interdependence of clinical evidence are taken into account. Moreover, an evidence weight training module is constructed. The ER Rule-based prediction model has superior performance compared with logistic regression and artificial neural network models. An innovative and pragmatic ER Rule-based CDSS for trauma outcome prediction is contributed by this article. In the era of big data, this CDSS helps predict patient outcomes based on historical data and helps physicians in emergency departments make proper trauma management decisions.
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Solving multiple-criteria R&D project selection problems with a data-driven evidential Reasoning Rule
International Journal of Project Management, 2019Co-Authors: Fang Liu, Yu-wang Chen, Jian-bo Yang, Weishu LiuAbstract:Abstract In this paper, a likelihood based evidence acquisition approach is proposed to acquire evidence from experts' assessments as recorded in historical datasets. Then a data-driven evidential Reasoning Rule based model is introduced to R&D project selection process by combining multiple pieces of evidence with different weights and reliabilities. As a result, the total belief degrees and the overall performance can be generated for ranking and selecting projects. Finally, a case study on the R&D project selection for the National Natural Science Foundation of China is conducted to show the effectiveness of the proposed model. The data-driven evidential Reasoning Rule based model for project evaluation and selection (1) utilizes experimental data to represent experts' assessments by using belief distributions over the set of final funding outcomes, and through this historical statistics it helps experts and applicants to understand the funding probability to a given assessment grade, (2) implies the mapping relationships between the evaluation grades and the final funding outcomes by using historical data, and (3) provides a way to make fair decisions by taking experts' reliabilities into account. In the data-driven evidential Reasoning Rule based model, experts play different roles in accordance with their reliabilities which are determined by their previous review track records, and the selection process is made interpretable and fairer. The newly proposed model reduces the time-consuming panel review work for both managers and experts, and significantly improves the efficiency and quality of project selection process. Although the model is demonstrated for project selection in the NSFC, it can be generalized to other funding agencies or industries.
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evidential Reasoning Rule for madm with both weights and reliabilities in group decision making
Knowledge Based Systems, 2017Co-Authors: Mi Zhou, Yu-wang Chen, Jian-bo Yang, Xin Bao LiuAbstract:Abstract Multiple attribute decision making (MADM) problems often include quantitative and qualitative attributes which can be assessed by numerical values and subjective judgements respectively. The evidential Reasoning (ER) Rule provides a process for dealing with this type of MADM problems of both a quantitative and qualitative nature of uncertainty. In this paper, the ER Rule is generalized to dealing with MADM problems in group decision making circumstance where the weights and reliabilities of both experts and attributes are considered. Specifically, the result and process aggregation based ER Rules for MADM in group decision making are given respectively, followed by the comparative analysis on the given aggregations. The ER analytical Rule for group MADM problems is also provided for the generalization of the ER analytical approach where group decision making is not considered. It is also a development of Yang's ER Rule which is a recursive calculation process. Due to the fact that uncertainty and ambiguity are always existent in group decision making, interval weights and reliabilities of experts and attributes should be taken into account in the process of experts’ judgment aggregation. In this paper, several ER based programming models under interval weights and reliabilities are constructed for the generation of global belief degrees in a consistent way. A case study is conducted on the life cycle assessment of electric vehicles to illustrate the applicability of the proposed method and the potential in supporting MADM in group decision making.
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evaluation ranking and selection of r d projects by multiple experts an evidential Reasoning Rule based approach
Scientometrics, 2017Co-Authors: Fang Liu, Yu-wang Chen, Weidong Zhu, Jian-bo YangAbstract:As a typical multi-criteria group decision making (MCGDM) problem, research and development (R&D) project selection involves multiple decision criteria which are formulated by different frames of discernment, and multiple experts who are associated with different weights and reliabilities. The evidential Reasoning (ER) Rule is a rational and rigorous approach to deal with such MCGDM problems and can generate comprehensive distributed evaluation outcomes for each R&D project. In this paper, an ER Rule based model taking into consideration experts' weights and reliabilities is proposed for R&D project selection. In the proposed approach, a utility based information transformation technique is applied to handle qualitative evaluation criteria with different evaluation grades, and both adaptive weights of criteria and utilities assigned to evaluation grades are introduced to the ER Rule based model. A nonlinear optimisation model is developed for the training of weights and utilities. A case study with the National Science Foundation of China is conducted to demonstrate how the proposed method can be used to support R&D project selection. Validation data show that the evaluation results become more reliable and consistent with reality by using the trained weights and utilities from historical data.
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Data classification using evidence Reasoning Rule
Knowledge-Based Systems, 2017Co-Authors: Jin Zheng, Jian-bo Yang, Yu-wang ChenAbstract:In Dempster-Shafer evidence theory (DST) based classifier design, Dempster's combination (DC) Rule is commonly used as a multi-attribute classifier to combine evidence collected from different attributes. The main aim of this paper is to present a classification method using a novel combination Rule i.e., the evidence Reasoning (ER) Rule. As an improvement of the DC Rule, the newly proposed ER Rule defines the reliability and weight of evidence. The former indicates the ability of attribute or its evidence to provide correct assessment for classification problem, and the latter reflects the relative importance of evidence in comparison with other evidence when they need to be combined. The ER Rule-based classification procedure is expatiated from evidence acquisition and estimation of evidence reliability and weight to combination of evidence. It is a purely data-driven approach without making any assumptions about the relationships between attributes and class memberships, and the specific statistic distributions of attribute data. Experiential results on five popular benchmark databases taken from University of California Irvine (UCI) machine learning database show high classification accuracy that is competitive with other classical and mainstream classifiers.
Yu-wang Chen - One of the best experts on this subject based on the ideXlab platform.
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Solving multiple-criteria R&D project selection problems with a data-driven evidential Reasoning Rule
International Journal of Project Management, 2019Co-Authors: Fang Liu, Yu-wang Chen, Jian-bo Yang, Weishu LiuAbstract:Abstract In this paper, a likelihood based evidence acquisition approach is proposed to acquire evidence from experts' assessments as recorded in historical datasets. Then a data-driven evidential Reasoning Rule based model is introduced to R&D project selection process by combining multiple pieces of evidence with different weights and reliabilities. As a result, the total belief degrees and the overall performance can be generated for ranking and selecting projects. Finally, a case study on the R&D project selection for the National Natural Science Foundation of China is conducted to show the effectiveness of the proposed model. The data-driven evidential Reasoning Rule based model for project evaluation and selection (1) utilizes experimental data to represent experts' assessments by using belief distributions over the set of final funding outcomes, and through this historical statistics it helps experts and applicants to understand the funding probability to a given assessment grade, (2) implies the mapping relationships between the evaluation grades and the final funding outcomes by using historical data, and (3) provides a way to make fair decisions by taking experts' reliabilities into account. In the data-driven evidential Reasoning Rule based model, experts play different roles in accordance with their reliabilities which are determined by their previous review track records, and the selection process is made interpretable and fairer. The newly proposed model reduces the time-consuming panel review work for both managers and experts, and significantly improves the efficiency and quality of project selection process. Although the model is demonstrated for project selection in the NSFC, it can be generalized to other funding agencies or industries.
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evidential Reasoning Rule for madm with both weights and reliabilities in group decision making
Knowledge Based Systems, 2017Co-Authors: Mi Zhou, Yu-wang Chen, Jian-bo Yang, Xin Bao LiuAbstract:Abstract Multiple attribute decision making (MADM) problems often include quantitative and qualitative attributes which can be assessed by numerical values and subjective judgements respectively. The evidential Reasoning (ER) Rule provides a process for dealing with this type of MADM problems of both a quantitative and qualitative nature of uncertainty. In this paper, the ER Rule is generalized to dealing with MADM problems in group decision making circumstance where the weights and reliabilities of both experts and attributes are considered. Specifically, the result and process aggregation based ER Rules for MADM in group decision making are given respectively, followed by the comparative analysis on the given aggregations. The ER analytical Rule for group MADM problems is also provided for the generalization of the ER analytical approach where group decision making is not considered. It is also a development of Yang's ER Rule which is a recursive calculation process. Due to the fact that uncertainty and ambiguity are always existent in group decision making, interval weights and reliabilities of experts and attributes should be taken into account in the process of experts’ judgment aggregation. In this paper, several ER based programming models under interval weights and reliabilities are constructed for the generation of global belief degrees in a consistent way. A case study is conducted on the life cycle assessment of electric vehicles to illustrate the applicability of the proposed method and the potential in supporting MADM in group decision making.
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evaluation ranking and selection of r d projects by multiple experts an evidential Reasoning Rule based approach
Scientometrics, 2017Co-Authors: Fang Liu, Yu-wang Chen, Weidong Zhu, Jian-bo YangAbstract:As a typical multi-criteria group decision making (MCGDM) problem, research and development (R&D) project selection involves multiple decision criteria which are formulated by different frames of discernment, and multiple experts who are associated with different weights and reliabilities. The evidential Reasoning (ER) Rule is a rational and rigorous approach to deal with such MCGDM problems and can generate comprehensive distributed evaluation outcomes for each R&D project. In this paper, an ER Rule based model taking into consideration experts' weights and reliabilities is proposed for R&D project selection. In the proposed approach, a utility based information transformation technique is applied to handle qualitative evaluation criteria with different evaluation grades, and both adaptive weights of criteria and utilities assigned to evaluation grades are introduced to the ER Rule based model. A nonlinear optimisation model is developed for the training of weights and utilities. A case study with the National Science Foundation of China is conducted to demonstrate how the proposed method can be used to support R&D project selection. Validation data show that the evaluation results become more reliable and consistent with reality by using the trained weights and utilities from historical data.
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Data classification using evidence Reasoning Rule
Knowledge-Based Systems, 2017Co-Authors: Jin Zheng, Jian-bo Yang, Yu-wang ChenAbstract:In Dempster-Shafer evidence theory (DST) based classifier design, Dempster's combination (DC) Rule is commonly used as a multi-attribute classifier to combine evidence collected from different attributes. The main aim of this paper is to present a classification method using a novel combination Rule i.e., the evidence Reasoning (ER) Rule. As an improvement of the DC Rule, the newly proposed ER Rule defines the reliability and weight of evidence. The former indicates the ability of attribute or its evidence to provide correct assessment for classification problem, and the latter reflects the relative importance of evidence in comparison with other evidence when they need to be combined. The ER Rule-based classification procedure is expatiated from evidence acquisition and estimation of evidence reliability and weight to combination of evidence. It is a purely data-driven approach without making any assumptions about the relationships between attributes and class memberships, and the specific statistic distributions of attribute data. Experiential results on five popular benchmark databases taken from University of California Irvine (UCI) machine learning database show high classification accuracy that is competitive with other classical and mainstream classifiers.
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research project evaluation and selection an evidential Reasoning Rule based method for aggregating peer review information with reliabilities
viXra, 2017Co-Authors: Weidong Zhu, Fang Liu, Yu-wang Chen, Jian-bo Yang, Dongpeng WangAbstract:Research project evaluation and selection is mainly concerned with evaluating a number of research projects and then choosing some of them for implementation. It involves a complex multiple-experts multiple-criteria decision making process.
Fang Liu - One of the best experts on this subject based on the ideXlab platform.
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Solving multiple-criteria R&D project selection problems with a data-driven evidential Reasoning Rule
International Journal of Project Management, 2019Co-Authors: Fang Liu, Yu-wang Chen, Jian-bo Yang, Weishu LiuAbstract:Abstract In this paper, a likelihood based evidence acquisition approach is proposed to acquire evidence from experts' assessments as recorded in historical datasets. Then a data-driven evidential Reasoning Rule based model is introduced to R&D project selection process by combining multiple pieces of evidence with different weights and reliabilities. As a result, the total belief degrees and the overall performance can be generated for ranking and selecting projects. Finally, a case study on the R&D project selection for the National Natural Science Foundation of China is conducted to show the effectiveness of the proposed model. The data-driven evidential Reasoning Rule based model for project evaluation and selection (1) utilizes experimental data to represent experts' assessments by using belief distributions over the set of final funding outcomes, and through this historical statistics it helps experts and applicants to understand the funding probability to a given assessment grade, (2) implies the mapping relationships between the evaluation grades and the final funding outcomes by using historical data, and (3) provides a way to make fair decisions by taking experts' reliabilities into account. In the data-driven evidential Reasoning Rule based model, experts play different roles in accordance with their reliabilities which are determined by their previous review track records, and the selection process is made interpretable and fairer. The newly proposed model reduces the time-consuming panel review work for both managers and experts, and significantly improves the efficiency and quality of project selection process. Although the model is demonstrated for project selection in the NSFC, it can be generalized to other funding agencies or industries.
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evaluation ranking and selection of r d projects by multiple experts an evidential Reasoning Rule based approach
Scientometrics, 2017Co-Authors: Fang Liu, Yu-wang Chen, Weidong Zhu, Jian-bo YangAbstract:As a typical multi-criteria group decision making (MCGDM) problem, research and development (R&D) project selection involves multiple decision criteria which are formulated by different frames of discernment, and multiple experts who are associated with different weights and reliabilities. The evidential Reasoning (ER) Rule is a rational and rigorous approach to deal with such MCGDM problems and can generate comprehensive distributed evaluation outcomes for each R&D project. In this paper, an ER Rule based model taking into consideration experts' weights and reliabilities is proposed for R&D project selection. In the proposed approach, a utility based information transformation technique is applied to handle qualitative evaluation criteria with different evaluation grades, and both adaptive weights of criteria and utilities assigned to evaluation grades are introduced to the ER Rule based model. A nonlinear optimisation model is developed for the training of weights and utilities. A case study with the National Science Foundation of China is conducted to demonstrate how the proposed method can be used to support R&D project selection. Validation data show that the evaluation results become more reliable and consistent with reality by using the trained weights and utilities from historical data.
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research project evaluation and selection an evidential Reasoning Rule based method for aggregating peer review information with reliabilities
viXra, 2017Co-Authors: Weidong Zhu, Fang Liu, Yu-wang Chen, Jian-bo Yang, Dongpeng WangAbstract:Research project evaluation and selection is mainly concerned with evaluating a number of research projects and then choosing some of them for implementation. It involves a complex multiple-experts multiple-criteria decision making process.
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research project evaluation and selection an evidential Reasoning Rule based method for aggregating peer review information with reliabilities
Scientometrics, 2015Co-Authors: Weidong Zhu, Fang Liu, Yu-wang Chen, Jian-bo Yang, Dongpeng WangAbstract:Research project evaluation and selection is mainly concerned with evaluating a number of research projects and then choosing some of them for implementation. It involves a complex multiple-experts multiple-criteria decision making process. Thus this paper presents an effective method for evaluating and selecting research projects by using the recently-developed evidential Reasoning (ER) Rule. The proposed ER Rule based evaluation and selection method mainly includes (1) using belief structures to represent peer review information provided by multiple experts, (2) employing a confusion matrix for generating experts' reliabilities, (3) implementing utility based information transformation to handle qualitative evaluation criteria with different evaluation grades, and (4) aggregating multiple experts' evaluation information on multiple criteria using the ER Rule. An experimental study on the evaluation and selection of research proposals submitted to the National Science Foundation of China demonstrates the applicability and effectiveness of the proposed method. The results show that (1) the ER Rule based method can provide consistent and informative support to make informed decisions, and (2) the reliabilities of the review information provided by different experts should be taken into account in a rational research project evaluation and selection process, as they have a significant influence to the selection of eligible projects for panel review.
Dongpeng Wang - One of the best experts on this subject based on the ideXlab platform.
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research project evaluation and selection an evidential Reasoning Rule based method for aggregating peer review information with reliabilities
viXra, 2017Co-Authors: Weidong Zhu, Fang Liu, Yu-wang Chen, Jian-bo Yang, Dongpeng WangAbstract:Research project evaluation and selection is mainly concerned with evaluating a number of research projects and then choosing some of them for implementation. It involves a complex multiple-experts multiple-criteria decision making process.
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research project evaluation and selection an evidential Reasoning Rule based method for aggregating peer review information with reliabilities
Scientometrics, 2015Co-Authors: Weidong Zhu, Fang Liu, Yu-wang Chen, Jian-bo Yang, Dongpeng WangAbstract:Research project evaluation and selection is mainly concerned with evaluating a number of research projects and then choosing some of them for implementation. It involves a complex multiple-experts multiple-criteria decision making process. Thus this paper presents an effective method for evaluating and selecting research projects by using the recently-developed evidential Reasoning (ER) Rule. The proposed ER Rule based evaluation and selection method mainly includes (1) using belief structures to represent peer review information provided by multiple experts, (2) employing a confusion matrix for generating experts' reliabilities, (3) implementing utility based information transformation to handle qualitative evaluation criteria with different evaluation grades, and (4) aggregating multiple experts' evaluation information on multiple criteria using the ER Rule. An experimental study on the evaluation and selection of research proposals submitted to the National Science Foundation of China demonstrates the applicability and effectiveness of the proposed method. The results show that (1) the ER Rule based method can provide consistent and informative support to make informed decisions, and (2) the reliabilities of the review information provided by different experts should be taken into account in a rational research project evaluation and selection process, as they have a significant influence to the selection of eligible projects for panel review.
Weidong Zhu - One of the best experts on this subject based on the ideXlab platform.
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evaluation ranking and selection of r d projects by multiple experts an evidential Reasoning Rule based approach
Scientometrics, 2017Co-Authors: Fang Liu, Yu-wang Chen, Weidong Zhu, Jian-bo YangAbstract:As a typical multi-criteria group decision making (MCGDM) problem, research and development (R&D) project selection involves multiple decision criteria which are formulated by different frames of discernment, and multiple experts who are associated with different weights and reliabilities. The evidential Reasoning (ER) Rule is a rational and rigorous approach to deal with such MCGDM problems and can generate comprehensive distributed evaluation outcomes for each R&D project. In this paper, an ER Rule based model taking into consideration experts' weights and reliabilities is proposed for R&D project selection. In the proposed approach, a utility based information transformation technique is applied to handle qualitative evaluation criteria with different evaluation grades, and both adaptive weights of criteria and utilities assigned to evaluation grades are introduced to the ER Rule based model. A nonlinear optimisation model is developed for the training of weights and utilities. A case study with the National Science Foundation of China is conducted to demonstrate how the proposed method can be used to support R&D project selection. Validation data show that the evaluation results become more reliable and consistent with reality by using the trained weights and utilities from historical data.
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research project evaluation and selection an evidential Reasoning Rule based method for aggregating peer review information with reliabilities
viXra, 2017Co-Authors: Weidong Zhu, Fang Liu, Yu-wang Chen, Jian-bo Yang, Dongpeng WangAbstract:Research project evaluation and selection is mainly concerned with evaluating a number of research projects and then choosing some of them for implementation. It involves a complex multiple-experts multiple-criteria decision making process.
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research project evaluation and selection an evidential Reasoning Rule based method for aggregating peer review information with reliabilities
Scientometrics, 2015Co-Authors: Weidong Zhu, Fang Liu, Yu-wang Chen, Jian-bo Yang, Dongpeng WangAbstract:Research project evaluation and selection is mainly concerned with evaluating a number of research projects and then choosing some of them for implementation. It involves a complex multiple-experts multiple-criteria decision making process. Thus this paper presents an effective method for evaluating and selecting research projects by using the recently-developed evidential Reasoning (ER) Rule. The proposed ER Rule based evaluation and selection method mainly includes (1) using belief structures to represent peer review information provided by multiple experts, (2) employing a confusion matrix for generating experts' reliabilities, (3) implementing utility based information transformation to handle qualitative evaluation criteria with different evaluation grades, and (4) aggregating multiple experts' evaluation information on multiple criteria using the ER Rule. An experimental study on the evaluation and selection of research proposals submitted to the National Science Foundation of China demonstrates the applicability and effectiveness of the proposed method. The results show that (1) the ER Rule based method can provide consistent and informative support to make informed decisions, and (2) the reliabilities of the review information provided by different experts should be taken into account in a rational research project evaluation and selection process, as they have a significant influence to the selection of eligible projects for panel review.
