Delineating Acidified Soils in the Jizera Mountains Region Using Fuzzy Classification

2008 ◽  
pp. 303-309 ◽  
Author(s):  
L. Boruvka ◽  
L. Pavlu ◽  
R. Vasat ◽  
V. Penizek ◽  
O. Drabek
Keyword(s):  
Fuzzy Classification

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.

The fuzzy structure of populations

2002 ◽  
Vol 80 (12) ◽  
pp. 2235-2241 ◽  
Author(s):  
James A Schaefer ◽  
Chris C Wilson
Keyword(s):  
Rangifer Tarandus ◽  
Human Perception ◽  
Fuzzy Classification ◽  
Resolving Power ◽  
Biological Organization ◽  
Philosophical Foundations ◽  
Living Things ◽  
Biological World ◽  
Multiple Clusters ◽  
Management And Conservation

The human perception of biological organization has profound implications for the study, management, and conservation of living things. Traditional methods of classification, which imply all-or-nothing group membership, are inconsistent with the modern synthesis, which stresses variability and unique individuals. We propose that fuzzy classification, which allows fractional membership in multiple clusters, can more realistically denote many forms of biological organization, such as populations. We used fuzzy clustering to depict the ambiguous structure of a migratory caribou (Rangifer tarandus) herd, based on affinities in space use, and walleye (Stizostedion vitreum) stocks, based on genetic dissimilarities among multilocus genotypes. In both cases, fuzzy memberships conveyed the degree of uncertainty of belonging while resolving cluster memberships for unambiguous and problematic individuals. Vagueness implies that borderline group identity cannot be remedied with more resolving power. Fuzzy classification is more in tune with the empirical and philosophical foundations of our discipline and can reconcile our need to classify with an inherently vague biological world.

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