Abstract
Fuzzy integrals have attracted the attention of many researchers as a solution for expressing the interactions between classifiers in multiple-classifier fusion. In a classifier fusion system based on fuzzy integrals, the fuzzy measures will have a major impact on a system’s performance. Much work has been carried out by numerous authors on how to determine the fuzzy measures to improve results. Our paper presents some new characteristics of multiple-classifier fusion based on fuzzy integrals. This paper discusses the conditions under which the fusion system must give the incorrect classification and that the fusion system can give the correct classification even if all classifiers have given an incorrect classification. It will be helpful for improving classifier fusion systems and designing classifiers in application.
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© 2006 Springer-Verlag Berlin Heidelberg
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Feng, H., Li, X., Fan, T., Chen, Y. (2006). Some Characteristics of Fuzzy Integrals as a Multiple Classifiers Fusion Method. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_106
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DOI: https://doi.org/10.1007/11739685_106
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
Online ISBN: 978-3-540-33585-6
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