A k-nearest neighbours method based on lower previsions

• Authors: Sebastien Destercke
• Venue: IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
• Year: 2010

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.