Abstract
This work presents a comparison of current research in the use of voting ensembles of classifiers in order to improve the accuracy of single classifiers and make the performance more robust against the difficulties that each individual classifier may have. Also, a number of combination rules are proposed. Different voting schemes are discussed and compared in order to study the performance of the ensemble in each task. The ensembles have been trained on real data available for benchmarking and also applied to a case study related to statistical description models of melodies for music genre recognition.
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Moreno-Seco, F., Iñesta, J.M., de León, P.J.P., Micó, L. (2006). Comparison of Classifier Fusion Methods for Classification in Pattern Recognition Tasks. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921_77
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DOI: https://doi.org/10.1007/11815921_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37236-3
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