scholarly journals Performance Evaluation of Fuzzy Rule-Based Systems With Class Priority For Medical Diagnosis Problems

2007 ◽  
Author(s):  
T. Nakashima ◽  
Y. Yokota ◽  
G. Schaefer ◽  
H. Ishibuchi
Keyword(s):  
Performance Evaluation ◽  
Medical Diagnosis ◽  
Fuzzy Rule ◽  
Rule Based ◽  
Rule Based Systems

An Interpretability-Accuracy Tradeoff in Learning Parameters of Intuitionistic Fuzzy Rule-Based Systems

2016 ◽  
Vol 20 (5) ◽  
pp. 773-787
Author(s):  
Yanni Wang ◽  
◽  
Yaping Dai ◽  
Yu-Wang Chen ◽  
Witold Pedrycz ◽  
...  
Keyword(s):  
Medical Diagnosis ◽  
Fuzzy Rule ◽  
Programming Method ◽  
Parameter Learning ◽  
Data Set ◽  
Rule Based ◽  
Intuitionistic Fuzzy ◽  
Objective Programming ◽  
Accuracy Tradeoff ◽  
Rule Based Systems

Parameter learning of Intuitionistic Fuzzy Rule-Based Systems (IFRBSs) is discussed and applied to medical diagnosis with intent of establishing a sound tradeoff between interpretability and accuracy. This study aims to improve the accuracy of IFRBSs without sacrificing its interpretability. This paper proposes an Objective Programming Method with an Interpretability-Accuracy tradeoff (OPMIA) to learn the parameters of IFRBSs by tuning the types of membership and non-membership functions and by adjusting adaptive factors and rule weights. The proposed method has been validated in the context of a medical diagnosis problem and a well-known publicly available auto-mpg data set. Furthermore, the proposed method is compared to Objective Programming Method not considering the interpretability (OPMNI) and Objective Programming Method based on Similarity Measure (OPMSM). The OPMIA helps achieve a sound a tradeoff between accuracy and interpretability and demonstrates its advantages over the other two methods.

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.

scholarly journals A Fuzzy Rule-Based Expert System for Evaluating Intellectual Capital

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Mohammad Hossein Fazel Zarandi ◽  
Neda Mohammadhasan ◽  
Susan Bastani
Keyword(s):  
Performance Evaluation ◽  
Expert System ◽  
Intellectual Capital ◽  
Fuzzy Rule ◽  
Qualitative Evaluation ◽  
Linguistic Variables ◽  
Rule Based ◽  
Proposed Model ◽  
Linguistic Approach ◽  
Fuzzy Linguistic

A fuzzy rule-based expert system is developed for evaluating intellectual capital. A fuzzy linguistic approach assists managers to understand and evaluate the level of each intellectual capital item. The proposed fuzzy rule-based expert system applies fuzzy linguistic variables to express the level of qualitative evaluation and criteria of experts. Feasibility of the proposed model is demonstrated by the result of intellectual capital performance evaluation for a sample company.

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