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 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
Expert Systems with Applications: An International Journal
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In this contribution we carry out an analysis of the Fuzzy Reasoning Methods for Fuzzy Rule Based Classification Systems in the framework of balanced and imbalanced data-sets with different degrees of imbalance. We analyze the behaviour of the Fuzzy Rule Based Classification Systems searching for the best type of Fuzzy Reasoning Method in each case, also studying the cooperation of some pre-processing methods of instances for imbalanced data-sets. To do so we use a fuzzy rule learning method that extends the well-known Wang and Mendel algorithm to classification problems. The results obtained show the necessity to apply an instance preprocessing step and the differences for the most appropriate Fuzzy Reasoning Method in balanced and imbalanced data-sets, concluding that the choice of the Fuzzy Reasoning Method depends on the degree of imbalance, being the most adequate the use of the Winning Rule for high imbalanced data-sets and the Additive Combination method for the remaining data-sets.