Abstract
K-nearest neighbours algorithms are among the most popular existing classification methods, due to their simplicity and good performances. Over the years, several extensions of the initial method have been proposed. In this paper, we propose a K-nearest neighbours approach that uses the theory of imprecise probabilities, and more specifically lower previsions. This approach handles very generic models when representing imperfect information on the labels of training data, and decision rules developed within this theory allows to deal with issues related to the presence of conflicting information or to the absence of close neighbours. We also show that results of the classical voting K-NN procedures and distance-weighted k-NN procedures can be retrieved.
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Destercke, S. (2010). A K-Nearest Neighbours Method Based on Lower Previsions. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Computational Intelligence for Knowledge-Based Systems Design. IPMU 2010. Lecture Notes in Computer Science(), vol 6178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14049-5_14
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DOI: https://doi.org/10.1007/978-3-642-14049-5_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14048-8
Online ISBN: 978-3-642-14049-5
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