Genetic Fuzzy Rule Selection with Pareto-optimal Rules as Candidate Rules

Use of Pareto-Optimal and Near Pareto-Optimal Candidate Rules in Genetic Fuzzy Rule Selection

Genetic fuzzy rule selection is an effective approach to the design of accurate and interpretable fuzzy rule-based classifiers. It tries to minimize the complexity of fuzzy rule-based classifiers while maximizing their accuracy by selecting only a small number of fuzzy rules from a large number of candidate rules. One important issue in genetic fuzzy rule selection is the prescreening of candid...

Quadrature Rules for Fuzzy Integrals

Edge Detection Based On Nearest Neighbor Linear Cellular Automata Rules and Fuzzy Rule Based System

 Edge Detection is an important task for sharpening the boundary of images to detect the region of interest. This paper applies a linear cellular automata rules and a Mamdani Fuzzy inference model for edge detection in both monochromatic and the RGB images. In the uniform cellular automata a transition matrix has been developed for edge detection. The Results have been compared to the ...

Edge Detection Based On Nearest Neighbor Linear Cellular Automata Rules and Fuzzy Rule Based System

 Edge Detection is an important task for sharpening the boundary of images to detect the region of interest. This paper applies a linear cellular automata rules and a Mamdani Fuzzy inference model for edge detection in both monochromatic and the RGB images. In the uniform cellular automata a transition matrix has been developed for edge detection. The Results have been compared to the ...

Business Rule Learning with Interactive Selection of Association Rules

This paper presents the implementation of a classification system based on learning of association rules in conjunction with Drools rule engine. The rules are interactively discovered with a web-based data mining system EasyMiner.eu. The rules are approved and edited by the domain expert before they are deployed for classification.