Journal
PATTERN RECOGNITION LETTERS
Volume 22, Issue 1, Pages 25-33Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/S0167-8655(00)00096-9
Keywords
combination of classifiers; design of multiple classifier systems; accuracy and error diversity in classifier ensembles; image classification; remote sensing
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Multiple classifier systems (MCSs) based on the combination of outputs of a set of different classifiers have been proposed in the field of pattern recognition 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 are accurate and make different errors. Therefore, the fundamental need for methods aimed to design accurate and diverse 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 at selecting the subset made up of the most accurate and diverse classifiers. A proof of the optimality of the proposed design approach is given. Reported results on the classification of multisensor remote sensing images show that this approach allows the design of effective multiple classifier systems. (C) 2001 Elsevier Science B.V. All rights reserved.
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