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Optimization algorithm for learning consistent belief rule-base from examples

(2011) JOURNAL OF GLOBAL OPTIMIZATION. 51(2). p.255-270
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Organization
Abstract
A belief rule-based inference approach and its corresponding optimization algorithm deal with a rule-base with a belief structure called a belief rule base (BRB) that forms a basis in the inference mechanism. In this paper, a new learning method is proposed based on the given sample data for optimally generating a consistent BRB. The focus is given on the consistency of BRB knowing that the consistency conditions are often violated if the system is generated from real world data. The measurement of BRB inconsistency is incorporated in the objective function of the optimization algorithm. This process is formulated as a non-linear constraint optimization problem and solved using the optimization tool provided in MATLAB. A numerical example is demonstrated the effectiveness of the proposed algorithm.
Keywords
EVIDENTIAL REASONING APPROACH, Learning, MULTIATTRIBUTE DECISION-ANALYSIS, FUZZY-SETS, SAFETY ANALYSIS, INFERENCE, METHODOLOGY, UNCERTAINTY, SYSTEMS, Consistency, Belief rule base, Optimization

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Please use this url to cite or link to this publication:

MLA
Liu, Jun, Luis Martínez, Da Ruan, et al. “Optimization Algorithm for Learning Consistent Belief Rule-base from Examples.” JOURNAL OF GLOBAL OPTIMIZATION 51.2 (2011): 255–270. Print.
APA
Liu, Jun, Martínez, L., Ruan, D., Rodriguez, R., & Calzada, A. (2011). Optimization algorithm for learning consistent belief rule-base from examples. JOURNAL OF GLOBAL OPTIMIZATION, 51(2), 255–270.
Chicago author-date
Liu, Jun, Luis Martínez, Da Ruan, Rosa Rodriguez, and Alberto Calzada. 2011. “Optimization Algorithm for Learning Consistent Belief Rule-base from Examples.” Journal of Global Optimization 51 (2): 255–270.
Chicago author-date (all authors)
Liu, Jun, Luis Martínez, Da Ruan, Rosa Rodriguez, and Alberto Calzada. 2011. “Optimization Algorithm for Learning Consistent Belief Rule-base from Examples.” Journal of Global Optimization 51 (2): 255–270.
Vancouver
1.
Liu J, Martínez L, Ruan D, Rodriguez R, Calzada A. Optimization algorithm for learning consistent belief rule-base from examples. JOURNAL OF GLOBAL OPTIMIZATION. 2011;51(2):255–70.
IEEE
[1]
J. Liu, L. Martínez, D. Ruan, R. Rodriguez, and A. Calzada, “Optimization algorithm for learning consistent belief rule-base from examples,” JOURNAL OF GLOBAL OPTIMIZATION, vol. 51, no. 2, pp. 255–270, 2011.
@article{2918433,
  abstract     = {A belief rule-based inference approach and its corresponding optimization algorithm deal with a rule-base with a belief structure called a belief rule base (BRB) that forms a basis in the inference mechanism. In this paper, a new learning method is proposed based on the given sample data for optimally generating a consistent BRB. The focus is given on the consistency of BRB knowing that the consistency conditions are often violated if the system is generated from real world data. The measurement of BRB inconsistency is incorporated in the objective function of the optimization algorithm. This process is formulated as a non-linear constraint optimization problem and solved using the optimization tool provided in MATLAB. A numerical example is demonstrated the effectiveness of the proposed algorithm.},
  author       = {Liu, Jun and Martínez, Luis and Ruan, Da and Rodriguez, Rosa and Calzada, Alberto},
  issn         = {0925-5001},
  journal      = {JOURNAL OF GLOBAL OPTIMIZATION},
  keywords     = {EVIDENTIAL REASONING APPROACH,Learning,MULTIATTRIBUTE DECISION-ANALYSIS,FUZZY-SETS,SAFETY ANALYSIS,INFERENCE,METHODOLOGY,UNCERTAINTY,SYSTEMS,Consistency,Belief rule base,Optimization},
  language     = {eng},
  number       = {2},
  pages        = {255--270},
  title        = {Optimization algorithm for learning consistent belief rule-base from examples},
  url          = {http://dx.doi.org/10.1007/s10898-010-9605-x},
  volume       = {51},
  year         = {2011},
}

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