Abstract
The establishment of the learning data base is a long and tedious task that must be carried out before starting the classification process. An Evidential KNN (EvKNN) has been developed in order to help the user, which proposes the “best” samples to label according to a strategy. However, at the beginning of this task, the classes are not clearly defined and are represented by a number of labeled samples smaller than the k required samples for EvKNN. In this paper, we propose to take into account the available information on the classes using an adapted evidential model. The algorithm presented in this paper has been tested on the classification of an image collection.
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© 2012 Springer-Verlag Berlin Heidelberg
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Tim, S.C.W., Rombaut, M., Pellerin, D. (2012). Adaptive Initialization of a EvKNN Classification Algorithm. 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_8
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DOI: https://doi.org/10.1007/978-3-642-29461-7_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29460-0
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