A note on rule representation in expert systems
Information Sciences: an International Journal
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Cardinality, quantifiers, and the aggregation of fuzzy criteria
Fuzzy Sets and Systems - Special issue on fuzzy information processing
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Fuzzy classifiers for imbalanced data sets
Fuzzy classifiers for imbalanced data sets
Fuzzy methods in machine learning and data mining: Status and prospects
Fuzzy Sets and Systems
Fuzzy Classifier Design
Fuzzy set-based methods in instance-based reasoning
IEEE Transactions on Fuzzy Systems
The effect of class imbalance, complexity, size, and learning distribution on classifier performance
International Journal of Advanced Intelligence Paradigms
A review of information fusion techniques employed in iris recognition systems
International Journal of Advanced Intelligence Paradigms
Fuzzy rule-based segmentation of CT brain images of hemorrhage for compression
International Journal of Advanced Intelligence Paradigms
Fuzzy numbers from raw discrete data using linear regression
Information Sciences: an International Journal
Towards new directions of data mining by evolutionary fuzzy rules and symbolic regression
Computers & Mathematics with Applications
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Using the mass assignment mechanism, a fuzzy classifier can be derived directly from the class relative frequency distribution. Moreover, in this framework, a family of fuzzy sets can represent a class, thus adapting the classifier to the need of classification. Graduality and the corresponding concept of error can be used to guide the process of deriving class representing fuzzy sets. The classification algorithm is attractive due to its low complexity. Successful applications include imbalanced data classification problems where the class having fewer examples is the class of interest.