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[期刊论文]

Belief rule based expert system for classification problems with new rule activation and weight calculation procedures

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author:

Chang, Leilei (Chang, Leilei.) [1] | Zhou, ZhiJie (Zhou, ZhiJie.) [2] | You, Yuan (You, Yuan.) [3] | Unfold

Indexed by:

EI Scopus SCIE

Abstract:

Classification problems are significant because they constitute a meta-model for multiple theoretical and practical applications from a wide range of fields. The belief rule based (BRB) expert system has shown potentials in dealing with both quantitative and qualitative information under uncertainty. In this study, a BRB classifier is proposed to solve the classification problem. However, two challenges must be addressed. First, the size of the BRB classifier must be controlled within a feasible range for better expert involvement. Second, the initial parameters of the BRB classifier must be optimized by learning from the experts' knowledge and/or historic data. Therefore, new rule activation and weight calculation procedures are proposed to downsize the BRB classifier while maintaining the matching degree calculation procedure. Moreover, the optimal algorithm using the evidential reasoning (ER) algorithm as the inference engine and the differential evolution (DE) algorithm as the optimization engine is proposed to identify the fittest parameters, including the referenced values of the antecedent attributes, the weights of the rules and the beliefs of the degrees in the conclusion. Five benchmarks, namely, iris, wine, glass, cancer and pima, are studied to validate the efficiency of the proposed BRB classifier. The result shows that all five benchmarks could be precisely modeled with a limited number of rules. The proposed BRB classifier has also shown superior performance in comparing it with the results in the literature. (C) 2015 Elsevier Inc. All rights reserved.

Keyword:

Belief rule base Classification problems Optimization algorithm

Community:

  • [ 1 ] [Chang, Leilei]High Tech Inst Xian, Xian 710025, Shaanxi, Peoples R China
  • [ 2 ] [Zhou, ZhiJie]High Tech Inst Xian, Xian 710025, Shaanxi, Peoples R China
  • [ 3 ] [You, Yuan]High Tech Inst Xian, Xian 710025, Shaanxi, Peoples R China
  • [ 4 ] [Yang, Longhao]Fuzhou Univ, Decis Sci Inst, Fuzhou 350116, Peoples R China
  • [ 5 ] [Zhou, Zhiguo]Univ Texas SW Med Ctr Dallas, Dept Radiat Oncol, Dallas, TX 75235 USA

Reprint 's Address:

  • [Zhou, ZhiJie]High Tech Inst Xian, Xian 710025, Shaanxi, Peoples R China

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Source :

INFORMATION SCIENCES

ISSN: 0020-0255

Year: 2016

Volume: 336

Page: 75-91

4 . 8 3 2

JCR@2016

0 . 0 0 0

JCR@2023

ESI Discipline: COMPUTER SCIENCE;

ESI HC Threshold:175

JCR Journal Grade:1

CAS Journal Grade:1

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 100

30 Days PV: 3

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