A K-nearest neighbours method based on imprecise probabilities

A K-nearest neighbours method based on imprecise probabilities

K-nearest neighbours algorithms are among the most popular existing classification methods, due to their simplicity and their 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. We show that the proposed ap...

Nearest Neighbours without k: A Classification Formalism Based on Probability

Extended k-Nearest Neighbours based on Evidence Theory

An evidence theoretic classification method is proposed in this paper. In order to classify a pattern we consider its neighbours, which are taken as parts of a single source of evidence to support the class membership of the pattern. A single mass function or basic belief assignment is then derived, and the belief function and the pignistic (“betting rates”) probability function can be calculat...

Semantic Text Categorization using the K Nearest Neighbours Method

In this paper we investigate the influence of semantics in the text categorization. Moreover, we study how the vocabulary size reduction affects this task. The K Nearest Neighbours method was used to perform the categorization. In order to reduce the vocabulary size, the Information Gain technique was employed. A number of different document codification alternatives were tested. The experiment...

A Variable Metric Probabilistic k-Nearest-Neighbours Classifier

12:06 30th March 2004 Abstract The k -nearest neighbour (k -nn) model is a simple, popular classifier. Probabilistic k -nn is a more powerful variant in which the model is cast in a Bayesian framework using (reversible jump) Markov chain Monte Carlo methods to average out the uncertainy over the model parameters. The k -nn classifier depends crucially on the metric used to determine distances b...