Fuzzy regularized generalized eigenvalue classifier with a novel membership function

Supervised classification of data affected by noise or error, with unknown probability distribution, is a challenging task. To this extend, we propose the Fuzzy Regularized Eigenvalue Classifier, based on a recent technique to classify data in two or more classes. We compare the execution time and a...

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Bibliographic Details
Published in:Information sciences 2013-10, Vol.245, p.53-62
Main Authors: Guarracino, Mario Rosario, Irpino, Antonio, Jasinevicius, Raimundas, Verde, Rosanna
Format: Article
Language:English
Subjects:
Membership function
Regularized generalized eigenvalue classifier
Supervised classification
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Summary:Supervised classification of data affected by noise or error, with unknown probability distribution, is a challenging task. To this extend, we propose the Fuzzy Regularized Eigenvalue Classifier, based on a recent technique to classify data in two or more classes. We compare the execution time and accuracy of the classifier with other de facto standard methods. With the adoption of a novel membership function, the classifier is capable to produce more accurate models that well compare with results obtained by other methods, and fuzzy weighting functions, on benchmark datasets.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2013.03.058