Using special structured fuzzy measure to represent interaction among IF-THEN rules

Authors:
Xi-Zhao Wang;Jun Shen
Affiliations:
Machine Learning Center, Faculty of Mathematics and Computer Science, Hebei University, Baoding, China;Machine Learning Center, Faculty of Mathematics and Computer Science, Hebei University, Baoding, China
Venue:
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Year:
2005

Citing 8
Cited 0

The representation of importance and interaction of features by fuzzy measures

Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Weighted fuzzy production rules

Fuzzy Sets and Systems
A genetic algorithm for determining nonadditive set functions in information fusion

Fuzzy Sets and Systems - Special issue on fuzzy measures and integrals
Weighted Fuzzy Reasoning Using Weighted Fuzzy Petri Nets

IEEE Transactions on Knowledge and Data Engineering
Genetic algorithms for determining fuzzy measures from data

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
A comparative study on similarity-based fuzzy reasoning methods

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Handling interaction in fuzzy production rule reasoning

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A multilevel weighted fuzzy reasoning algorithm for expert systems

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Quantified Score

Hi-index	0.00

Visualization

Abstract

When fuzzy IF-THEN rules are used to approximate reasoning, interaction exists among rules. Handling the interaction based on a non-integral can lead to an improvement of reasoning accuracy but the determination of non-linear integral usually needs to solve a linear programming problem with too many parameters when the rules are a little many. That is, the number of parameters increases exponentially with the number of rules. This paper proposes a new approach to denoting the interaction by a 2-additive fuzzy measure which replaces the general set function of the old non-linear integral approach. The number of parameters determined in the new approach is greatly less than the number of parameters in the old approach. Compared with the old approach, the new one has a little loss of accuracy but the new approach reduces the number of parameters from an exponential to polynomial quantity.