Abstract
A nonparametric affine-invariant extrapolation depth-based classifier resistant to spikes and extreme values is proposed and investigated. A multilevel smoothing structure is proposed that makes it possible to obtain global properties of density functions and class boundaries under appropriate regularity conditions. The extrapolation depth-based classifier uses kernel density estimates to efficiently classify multidimensional data at different smoothing levels.
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Translated from Kibernetika Sistemnyi Analiz, No. 2, March–April, 2016, pp. 64–72.
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Galkin, O.A. Affine-Invariant Classifier of Extrapolation Depth on the Basis of a Multilevel Smoothing Structure. Cybern Syst Anal 52, 232–239 (2016). https://doi.org/10.1007/s10559-016-9819-0
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DOI: https://doi.org/10.1007/s10559-016-9819-0