Abstract
In this paper, we propose to fuse both clustering and supervised classification approach in order to outperform the results of a classification algorithm. Indeed the results of the learning in supervised classification depend on the method and on the parameters chosen. Moreover the learning process is particularly difficult which few learning data and/or imprecise learning data. Hence, we define a classification approach using the theory of belief functions to fuse the results of one clustering and one supervised classification. This new approach applied on real databases allows good and promising results.
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Karem, F., Dhibi, M., Martin, A. (2012). Combination of Supervised and Unsupervised Classification Using the Theory of Belief Functions. In: Denoeux, T., Masson, MH. (eds) Belief Functions: Theory and Applications. Advances in Intelligent and Soft Computing, vol 164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29461-7_10
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DOI: https://doi.org/10.1007/978-3-642-29461-7_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29460-0
Online ISBN: 978-3-642-29461-7
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