Data-driven fuzzy sets for classification
Sofia Visa, Anca Ralescu · International Journal of Advanced Intelligence Paradigms · 2008
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.