Prototype classification and feature selection with fuzzy sets
- 1 February 1977
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Systems, Man, and Cybernetics
- Vol. 7 (2), 87-92
- https://doi.org/10.1109/tsmc.1977.4309659
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.Keywords
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