Abstract
There are two main approaches to combine the output of classifiers within a multi-classifier system, which are: combination-based and selection-based methods. This paper presents an investigation of how the use of weights in some non-trainable simple combination-based methods applied to ensembles with different levels of diversity. It is aimed to analyse whether the use of weights can decrease the dependency of ensembles on the diversity of their members.
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Dutra, T., Canuto, A.M.P., de Souto, M.C.P. (2006). Using Weighted Combination-Based Methods in Ensembles with Different Levels of Diversity. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_79
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DOI: https://doi.org/10.1007/11893028_79
Publisher Name: Springer, Berlin, Heidelberg
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