Using Choquet integrals for kNN approximation and classification
Beliakov, Gleb and James, Simon 2008, Using Choquet integrals for kNN approximation and classification, in 2008 IEEE International Conference on Fuzzy Systems : proceedings : FUZZ-IEEE 2008, IEEE, Piscataway, N.J., pp. 1311-1317, doi: 10.1109/FUZZY.2008.4630542.
k-nearest neighbors (kNN) is a popular method for function approximation and classification. One drawback of this method is that the nearest neighbors can be all located on one side of the point in question x. An alternative natural neighbors method is expensive for more than three variables. In this paper we propose the use of the discrete Choquet integral for combining the values of the nearest neighbors so that redundant information is canceled out. We design a fuzzy measure based on location of the nearest neighbors, which favors neighbors located all around x.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Field of Research
080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective
970101 Expanding Knowledge in the Mathematical Sciences
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.
Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO.
If you believe that your rights have been infringed by this repository, please contact firstname.lastname@example.org.
Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact email@example.com.