Abstract
We introduce common framework for classifiers fusion methods using dynamic weights in decision making process. Both weighted average combiners with dynamic weights and combiners which dynamically estimate local competence are considered. Few algorithms presented in the literature are shown in accordance with our model. In addition we propose two new methods for combining classifiers. The problem of protein secondary structure prediction was selected as a benchmark test. Experiments were carried out on previously prepared dataset of non-homologous proteins for fusion algorithms comparison. The results have proved that developed framework generalizes dynamic weighting approaches and should be further investigated.
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Woloszynski, T., Kurzynski, M. (2006). Application of Combining Classifiers Using Dynamic Weights to the Protein Secondary Structure Prediction – Comparative Analysis of Fusion Methods. In: Maglaveras, N., Chouvarda, I., Koutkias, V., Brause, R. (eds) Biological and Medical Data Analysis. ISBMDA 2006. Lecture Notes in Computer Science(), vol 4345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11946465_8
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DOI: https://doi.org/10.1007/11946465_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68063-5
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