Title: Data-driven fuzzy sets for classification
Authors: Sofia Visa, Anca Ralescu
Addresses: Department of Computer Science, The College of Wooster, 1189 Beall Avenue, Wooster OH 44691, USA. ' Department of Computer Science, University of Cincinnati, ML 0030, 2600 Clifton Ave. Cincinnati, OH 45221-0030, USA
Abstract: 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.
Keywords: fuzzy sets; mass assignment; selection rules; fuzzy classifiers; graduality; error model; imbalanced data classification.
International Journal of Advanced Intelligence Paradigms, 2008 Vol.1 No.1, pp.3 - 30
Published online: 17 Oct 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article