Abstract
Choquet or Sugeno fuzzy integrals are commonly used for aggregating the results of different classifiers. However, both these integrals belong to a more general class of fuzzy t-conorm integrals. In this paper, we describe a framework of a fuzzy t-conorm integral and its use for combining classifiers. We show the advantages of this approach to classifier combining in several benchmark tests.
The research reported in this paper was partially supported by the Program “Information Society” under project 1ET100300517 (D. Štefka) and the grant No. 201/05/0325 of the Grant Agency of the Czech Republic (M. Holeňa), and partially supported by the Institutional Research Plan AV0Z10300504.
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© 2007 Springer-Verlag Berlin Heidelberg
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Štefka, D., Holeňa, M. (2007). The Use of Fuzzy t-Conorm Integral for Combining Classifiers. In: Mellouli, K. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2007. Lecture Notes in Computer Science(), vol 4724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75256-1_66
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DOI: https://doi.org/10.1007/978-3-540-75256-1_66
Publisher Name: Springer, Berlin, Heidelberg
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