A parallel network of modified 1-NN classifiers and fuzzy k-NN classifiers is proposed. All the component classifiers decide between two classes only. They operate as follows. For each class i a certain area Ai is constructed. If the classified point lies outside of each area Ai, then the classification is refused. When it belongs only to one of the areas Ai, then the classification is being performed by 1-NN rule. Points that lie in an overlapping area of some areas Ai, are classified by the fuzzy k-NN rule with hard (nonfuzzy) output. Two feature selection sessions are recommended. One to minimise the size of overlapping areas, another to minimise an error rate for the fuzzy k-NN rule. The aim of this work is to create a classifier that is nearly as fast as 1-NN rule and which performance is as good as that for the fuzzy k-NN rule. The effectiveness of the proposed approach was verified on a real data set containing 5 classes, 15 features and 2440 objects.

Network of modified 1-NN and fuzzy k-NN classifiers in application to remote sensing image recognition

ROLI, FABIO;
1994

Abstract

A parallel network of modified 1-NN classifiers and fuzzy k-NN classifiers is proposed. All the component classifiers decide between two classes only. They operate as follows. For each class i a certain area Ai is constructed. If the classified point lies outside of each area Ai, then the classification is refused. When it belongs only to one of the areas Ai, then the classification is being performed by 1-NN rule. Points that lie in an overlapping area of some areas Ai, are classified by the fuzzy k-NN rule with hard (nonfuzzy) output. Two feature selection sessions are recommended. One to minimise the size of overlapping areas, another to minimise an error rate for the fuzzy k-NN rule. The aim of this work is to create a classifier that is nearly as fast as 1-NN rule and which performance is as good as that for the fuzzy k-NN rule. The effectiveness of the proposed approach was verified on a real data set containing 5 classes, 15 features and 2440 objects.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/44037
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