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Design of Effective Multiple Classifier Systems by Clustering of Classifiers

9 years 2 months ago
Design of Effective Multiple Classifier Systems by Clustering of Classifiers
In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. Previous work clearly showed that multiple classifier systems are effective only if the classifiers forming them make independent errors. Therefore, the fundamental need for methods aimed to design “errorindependent” classifiers is currently acknowledged. In this paper, an approach to the automatic design of multiple classifier systems is proposed. Given an initial large set of classifiers, our approach is aimed to select the subset formed by the most error-independent classifiers. Reported results on the classification of multisensor remote-sensing images show that this approach allows to design effective multiple classifier systems.
Giorgio Giacinto, Fabio Roli, Giorgio Fumera
Added 31 Jul 2010
Updated 31 Jul 2010
Type Conference
Year 2000
Where ICPR
Authors Giorgio Giacinto, Fabio Roli, Giorgio Fumera
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