An Ensemble Approach for Extended Belief Rule-Based Systems with Parameter Optimization

DOI

10.2991/ijcis.d.191112.001How to use a DOI?

Keywords

Extended belief rule base; AdaBoost; Differential evolution algorithm

Abstract

The reasoning ability of the belief rule-based system is easy to be weakened by the quality of training instances, the inconsistency of rules and the values of parameters. This paper proposes an ensemble approach for extended belief rule-based systems to address this issue. The approach is based on the AdaBoost algorithm and the differential evolution (DE) algorithm. In the AdaBoost algorithm, the weights of samples are updated to allow the new subsequent subsystem to pay more attention to those samples misclassified by pervious system. And the DE algorithm is used as the parameter optimization engine to ensure the reasoning ability of the learned extended belief rule-based sub-systems. Since the learned sub-systems are complementary, the reasoning ability of the belief rule-based system can be boosted by combing these sub-systems. Some case studies about many classification test datasets are provided in this paper in the last. The feasibility and efficiency of the proposed approach has been proven by the experimental results.

Copyright

© 2019 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Hong-Yun Huang
AU  - Yan-Qing Lin
AU  - Qun Su
AU  - Xiao-Ting Gong
AU  - Ying-Ming Wang
AU  - Yang-Geng Fu
PY  - 2019
DA  - 2019/11/21
TI  - An Ensemble Approach for Extended Belief Rule-Based Systems with Parameter Optimization
JO  - International Journal of Computational Intelligence Systems
SP  - 1371
EP  - 1381
VL  - 12
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.191112.001
DO  - 10.2991/ijcis.d.191112.001
ID  - Huang2019
ER  -