scholarly journals VICToRy: Visual Interactive Consistency Management in Tolerant Rule-based Systems

2020 ◽  
Vol 330 ◽  
pp. 1-12
Author(s):  
Nils Weidmann ◽  
Anthony Anjorin ◽  
James Cheney
Keyword(s):  
Rule Based ◽  
Consistency Management ◽  
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.

Schema Compliant Consistency Management via Triple Graph Grammars and Integer Linear Programming

2021 ◽  
Author(s):  
Nils Weidmann ◽  
Anthony Anjorin
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Experimental Evaluation ◽  
Graph Grammars ◽  
Model Driven Engineering ◽  
Rule Based ◽  
Consistency Management ◽  
Model Driven ◽  
Triple Graph Grammars ◽  
Domain Constraints

AbstractIn the field of Model-Driven Engineering, Triple Graph Grammars (TGGs) play an important role as a rule-based means of implementing consistency management. From a declarative specification of a consistency relation, several operations including forward and backward transformations, (concurrent) synchronisation, and consistency checks can be automatically derived. For TGGs to be applicable in realistic application scenarios, expressiveness in terms of supported language features is very important. A TGG tool is schema compliant if it can take domain constraints, such as multiplicity constraints in a meta-model, into account when performing consistency management tasks. To guarantee schema compliance, most TGG tools allow application conditions to be attached as necessary to relevant rules. This strategy is problematic for at least two reasons: First, ensuring compliance to a sufficiently expressive schema for all previously mentioned derived operations is still an open challenge; to the best of our knowledge, all existing TGG tools only support a very restricted subset of application conditions. Second, it is conceptually demanding for the user to indirectly specify domain constraints as application conditions, especially because this has to be completely revisited every time the TGG or domain constraint is changed. While domain constraints can in theory be automatically transformed to obtain the required set of application conditions, this has only been successfully transferred to TGGs for a very limited subset of domain constraints. To address these limitations, this paper proposes a search-based strategy for achieving schema compliance. We show that all correctness and completeness properties, previously proven in a setting without domain constraints, still hold when schema compliance is to be additionally guaranteed. An implementation and experimental evaluation are provided to support our claim of practical applicability.

A Methodology for Building Fuzzy Rule-Based Systems Integrating Expert and Data Knowledge

2014 ◽  
Author(s):  
Helano Povoas de Lima ◽  
Heloisa de Arruda Camargo
Keyword(s):  
Fuzzy Rule ◽  
Rule Based ◽  
Rule Based Systems

Enhancing extended belief rule-based systems for classification problems using decomposition strategy and overlap function

2021 ◽  
Author(s):  
Long-Hao Yang ◽  
Jun Liu ◽  
Ying-Ming Wang ◽  
Hui Wang ◽  
Luis Martínez
Keyword(s):  
Classification Problems ◽  
Rule Based ◽  
Rule Based Systems

Type Inference for Hierarchical Multiset Structures in Rule-Based Systems

2021 ◽  
Author(s):  
Bogdan Aman ◽  
Gabriel Ciobanu
Keyword(s):  
Type Inference ◽  
Rule Based ◽  
Rule Based Systems
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