When a multiple classifier system is employed, one of the most popular methods to accomplish the classifier fusion is the simple majority voting. However, when the performance of the ensemble members is not uniform, the efficiency of this type of voting generally results affected negatively. In this paper, new functions for dynamic weighting in classifier fusion are introduced. Experimental results demonstrate the advantages of these novel strategies over the simple voting scheme. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Valdovinos, R. M., & Sánchez, J. S. (2009). Combining multiple classifiers with dynamic weighted voting. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5572 LNAI, pp. 510–516). https://doi.org/10.1007/978-3-642-02319-4_61
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