C4.5: programs for machine learning
C4.5: programs for machine learning
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
A genetic algorithm for generating fuzzy classification rules
Fuzzy Sets and Systems
Fuzzy Modelling: Paradigms and Practices
Fuzzy Modelling: Paradigms and Practices
The Effect of the Input Density Distribution on Kernel-based Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Supervised fuzzy clustering for the identification of fuzzy classifiers
Pattern Recognition Letters
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
International Journal of Approximate Reasoning
Fuzzy-rough nearest neighbor algorithms in classification
Fuzzy Sets and Systems
Fuzzy classifier design using genetic algorithms
Pattern Recognition
Knowledge acquisition from quantitative data using the rough-set theory
Intelligent Data Analysis
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
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In the recent years, rough set theory has been applied in diverse areas of research, however its application to classification problems is still a challenger. In this paper we present a new method to automatically generate fuzzy rules using an extension of rough sets. We use genetic algorithm to determine the granules of the knowledge to obtain the rough sets. The resulting classifier system based on the set of fuzzy rules was tested with the public databases: Iris, Wine, and Wdbc datasets, presenting accuracy rates of 100%, 100%, and 99%, respectively.