Knowledge Base Learning of Linguistic Fuzzy Rule-Based Systems in a Multi-objective Evolutionary Framework

2009 ◽  
pp. 747-754 ◽  
Author(s):  
P. Ducange ◽  
R. Alcalá ◽  
F. Herrera ◽  
B. Lazzerini ◽  
F. Marcelloni
Keyword(s):  
Knowledge Base ◽  
Fuzzy Rule ◽  
Rule Based ◽  
Multi Objective ◽  
Rule Based Systems

A Review of Multi-Objective Evolutionary Based Fuzzy Classifiers

2020 ◽  
Vol 13 (1) ◽  
pp. 77-85
Author(s):  
Praveen Kumar Dwivedi ◽  
Surya Prakash Tripathi
Keyword(s):  
Fuzzy Systems ◽  
Evolutionary Process ◽  
Fuzzy Rule ◽  
Fuzzy Classification ◽  
Multi Objective Optimization ◽  
Fuzzy Classifier ◽  
Rule Based ◽  
Trade Off ◽  
Multi Objective ◽  
Rule Based Systems

Background: Fuzzy systems are employed in several fields like data processing, regression, pattern recognition, classification and management as a result of their characteristic of handling uncertainty and explaining the feature of the advanced system while not involving a particular mathematical model. Fuzzy rule-based systems (FRBS) or fuzzy rule-based classifiers (mainly designed for classification purpose) are primarily the fuzzy systems that consist of a group of fuzzy logical rules and these FRBS are unit annexes of ancient rule-based systems, containing the "If-then" rules. During the design of any fuzzy systems, there are two main objectives, interpretability and accuracy, which are conflicting with each another, i.e., improvement in any of those two options causes the decrement in another. This condition is termed as Interpretability –Accuracy Trade-off. To handle this condition, Multi-Objective Evolutionary Algorithms (MOEA) are often applied within the design of fuzzy systems. This paper reviews the approaches to the problem of developing fuzzy systems victimization evolutionary process Multi-Objective Optimization (EMO) algorithms considering ‘Interpretability-Accuracy Trade-off, current research trends and improvement in the design of fuzzy classifier using MOEA in the future scope of authors. Methods: The state-of-the-art review has been conducted for various fuzzy classifier designs, and their optimization is reviewed in terms of multi-objective. Results: This article reviews the different Multi-Objective Optimization (EMO) algorithms in the context of Interpretability -Accuracy tradeoff during fuzzy classification. Conclusion: The evolutionary multi-objective algorithms are being deployed in the development of fuzzy systems. Improvement in the design using these algorithms include issues like higher spatiality, exponentially inhabited solution, I-A tradeoff, interpretability quantification, and describing the ability of the system of the fuzzy domain, etc. The focus of the authors in future is to find out the best evolutionary algorithm of multi-objective nature with efficiency and robustness, which will be applicable for developing the optimized fuzzy system with more accuracy and higher interpretability. More concentration will be on the creation of new metrics or parameters for the measurement of interpretability of fuzzy systems and new processes or methods of EMO for handling I-A tradeoff.

A MULTI-OBJECTIVE GENETIC ALGORITHM FOR TUNING AND RULE SELECTION TO OBTAIN ACCURATE AND COMPACT LINGUISTIC FUZZY RULE-BASED SYSTEMS

2007 ◽  
Vol 15 (05) ◽  
pp. 539-557 ◽  
Author(s):  
R. ALCALÁ ◽  
M. J. GACTO ◽  
F. HERRERA ◽  
J. ALCALÁ-FDEZ
Keyword(s):  
Fuzzy Rule ◽  
Membership Functions ◽  
Genetic Operators ◽  
Fuzzy Modelling ◽  
Rule Based ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Selection Of ◽  
Modelling Problems ◽  
Rule Based Systems

This work proposes the application of Multi-Objective Genetic Algorithms to obtain Fuzzy Rule-Based Systems with a better trade-off between interpretability and accuracy in linguistic fuzzy modelling problems. To do that, we present a new post-processing method that by considering selection of rules together with tuning of membership functions gets solutions only in the Pareto zone with the highest accuracy, i.e., containing solutions with the least number of possible rules but still presenting high accuracy. This method is based on the well-known SPEA2 algorithm, applying appropriate genetic operators and including some modifications to concentrate the search in the desired Pareto zone.

Multi-Objective Evolutionary Design of Fuzzy Rule-Based Systems

2016 ◽  
pp. 635-670 ◽  
Author(s):  
Michela Antonelli ◽  
Pietro Ducange ◽  
Francesco Marcelloni
Keyword(s):  
Fuzzy Rule ◽  
Evolutionary Design ◽  
Rule Based ◽  
Multi Objective ◽  
Rule Based Systems
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