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.
On classification with missing data using rough-neuro-fuzzy systems