[1]LIN Jin,HU Jiachen,LIU Wanling,et al.Belief rule base classification algorithm using MISA multi-objective optimization[J].CAAI Transactions on Intelligent Systems,2019,14(5):982-990.[doi:10.11992/tis.201809022]
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Belief rule base classification algorithm using MISA multi-objective optimization

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 5 Page number: 982-990 Column: 学术论文—机器学习 Public date: 2019-09-05
Title:
Belief rule base classification algorithm using MISA multi-objective optimization
Author(s):
LIN Jin;  HU Jiachen;  LIU Wanling;  WU Yingjie
College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China
Keywords:
belief rule base (BRB); classification system; multi-objective optimization; MISA; Pareto optimal; differential evolution; adaptive mesh; feature attribute reduction
CLC:
TP18
DOI:
10.11992/tis.201809022
Abstract:
The efficiency and accuracy of a belief rule base (BRB) classification system are limited to the determination of the systematic parameters and the structure of the rule base. In this study, we propose the usage of a BRB classification algorithm, i.e., the multi-objective immune system algorithm (MISA), along with multi-objective optimization to determine the optimal parameters and the structure of the rule base. This method simplifies the characteristic attributes using a differential evolution algorithm to develop a training model and subsequently uses MISA to optimize the systematic complexity and the classification accuracy for identifying an optimal solution for the classification model. In the experiment, we initially compare the results of the BRM-based MISA (MISA-BRM) and those of the BRB classification system with respect to their complexity and accuracy to present the benefits of our method. Further, we compare the results with those of the existing classification methods to verify the feasibility and availability of the proposed method. The experimental results denote that the proposed method can effectively optimize the accuracy and complexity of the BRB classification system.
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