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 "error-independent" classifiers is currently acknowledged. In the paper, an approach to the automatic design of multiple classifier systems is proposed. Given an initial large set of classifiers, our approach is aimed at selecting 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.