Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining (Advanced Information Processing)
A weighting function for improving fuzzy classification systems performance
Fuzzy Sets and Systems
A proposal on reasoning methods in fuzzy rule-based classification systems
International Journal of Approximate Reasoning
Effect of rule weights in fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Rule Weight Specification in Fuzzy Rule-Based Classification Systems
IEEE Transactions on Fuzzy Systems
A genetic design of linguistic terms for fuzzy rule based classifiers
International Journal of Approximate Reasoning
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In this contribution we carry out an analysis of the rule weights and Fuzzy Reasoning Methods for Fuzzy Rule Based Classification Systems in the framework of imbalanced data-sets with a high imbalance degree. We analyze the behaviour of the Fuzzy Rule Based Classification Systems searching for the best configuration of rule weight and Fuzzy Reasoning Method also studying the cooperation of some pre-processing methods of instances. To do so we use a simple rule base obtained with the Chi (and co-authors') method that extends the well-known Wang and Mendel method to classification problems.The results obtained show the necessity to apply an instance pre-processing step and the clear differences in the use of the rule weight and Fuzzy Reasoning Method.Finally, it is empirically proved that there is a superior performance of Fuzzy Rule Based Classification Systems compared to the 1-NN and C4.5 classifiers in the framework of highly imbalanced data-sets.