Distributed representation of fuzzy rules and its application to pattern classification
Fuzzy Sets and Systems
Applicability of the fuzzy operators in the design of fuzzy logic controllers
Fuzzy Sets and Systems
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems
Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems
Comparison of Heuristic Criteria for Fuzzy Rule Selection in Classification Problems
Fuzzy Optimization and Decision Making
Rule-based modeling: precision and transparency
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On generating FC3 fuzzy rule systems from data usingevolution strategies
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
Compact and transparent fuzzy models and classifiers through iterative complexity reduction
IEEE Transactions on Fuzzy Systems
Rule Weight Specification in Fuzzy Rule-Based Classification Systems
IEEE Transactions on Fuzzy Systems
Building classification rules for case-based classifier using fuzzy sets and formal concept analysis
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
A hierarchical approach to multi-class fuzzy classifiers
Expert Systems with Applications: An International Journal
A new fuzzy rule-based classification system for word sense disambiguation
Intelligent Data Analysis
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A fuzzy rule-based classification system (FRBCS) is one of the most popular approaches used in pattern classification problems. One advantage of a fuzzy rule-based system is its interpretability. However, we're faced with some challenges when generating the rule-base. In high dimensional problems, we can not generate every possible rule with respect to all antecedent combinations. In this paper, by making the use of some data mining concepts, we propose a method for rule generation, which can result in a rule-base containing rules of different lengths. As the next phase, we use rule-weight as a simple mechanism to tune the classifier and propose a new method of ruleweight specification for this purpose. Through computer simulations on some data sets from UCI repository, we show that the proposed scheme achieves better prediction accuracy compared with other fuzzy and non-fuzzy rule-based classification systems proposed in the past.