Prototype classification and feature selection with fuzzy sets

Abstract

The fuzzy ISODATA algorithms are used to address two problems: first, the question of feature selection for binary valued data sets is investigated; and second, the same method is applied to the design of a fuzzy one-nearest prototype classifier. The efficiency of this fuzzy classifier is compared to conventional k-NN classifiers by a computational example using the stomach disease data of Scheinok and Rupe, and Toussaint's method for estimation of the probability of misclassification: the fuzzy prototype classifier appears to decrease the error rate expected from all k-NN classifiers by roughly ten per cent.