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.
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.
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Field of Research
080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective
970101 Expanding Knowledge in the Mathematical Sciences
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