Abstract
| Language | English |
|---|---|
| Journal | Knowledge and Information Systems |
| Early online date | 22 May 2018 |
| DOIs | |
| Publication status | E-pub ahead of print - 22 May 2018 |
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New activation weight calculation and parameter optimisation for extended belief rule-based system based on sensitivity analysis. / Yang, Longhao; Liu, J.; Wang, Yingming; Luis, Martinez.
In: Knowledge and Information Systems, 22.05.2018.Research output: Contribution to journal › Article
TY - JOUR
T1 - New activation weight calculation and parameter optimisation for extended belief rule-based system based on sensitivity analysis
AU - Yang, Longhao
AU - Liu, J.
AU - Wang, Yingming
AU - Luis, Martinez
PY - 2018/5/22
Y1 - 2018/5/22
N2 - An extended belief rule-based (EBRB) system is superior to existing rule-based systems in managing several types of uncertain information and modeling complex issues effectively and efficiently. However, the accuracy and interpretability of the EBRB system still need to be enhanced by addressing the following shortcomings: the interpretability of the intermediate variables in the EBRB system should be definite and the system parameters must be effectively determined. Therefore, we distinguish discrete and continuous data types to perform sensitivity analysis twice: first, on the rule inference scheme to study the interpretability of individual matching degrees and activation weights; and second, on the rule generation scheme to examine the effect of utility values and attribute weights on the accuracy of the EBRB system. Based on the analyses, we propose a novel activation weight calculation method and parameter optimization method to enhance the interpretability and accuracy of the EBRB system, respectively. We then present three case studies to elucidate the effectiveness of the proposed methods. The results indicate that the enhanced EBRB system prevents counterintuitive and insensitive situations and obtains better accuracies than some studies.
AB - An extended belief rule-based (EBRB) system is superior to existing rule-based systems in managing several types of uncertain information and modeling complex issues effectively and efficiently. However, the accuracy and interpretability of the EBRB system still need to be enhanced by addressing the following shortcomings: the interpretability of the intermediate variables in the EBRB system should be definite and the system parameters must be effectively determined. Therefore, we distinguish discrete and continuous data types to perform sensitivity analysis twice: first, on the rule inference scheme to study the interpretability of individual matching degrees and activation weights; and second, on the rule generation scheme to examine the effect of utility values and attribute weights on the accuracy of the EBRB system. Based on the analyses, we propose a novel activation weight calculation method and parameter optimization method to enhance the interpretability and accuracy of the EBRB system, respectively. We then present three case studies to elucidate the effectiveness of the proposed methods. The results indicate that the enhanced EBRB system prevents counterintuitive and insensitive situations and obtains better accuracies than some studies.
U2 - 10.1007/s10115-018-1211-0
DO - 10.1007/s10115-018-1211-0
M3 - Article
JO - Knowledge and Information Systems
T2 - Knowledge and Information Systems
JF - Knowledge and Information Systems
SN - 0219-1377
ER -