Geodesic
$k$-Depth-nearest Neighbour Method and its Performance on Skew-normal Distributons
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica, Tome 52 (2013) no. 2, pp. 121-129.

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In the present paper we investigate performance of the $k$-depth-nearest classifier. This classifier, proposed recently by Vencálek, uses the concept of data depth to improve the classification method known as the $k$-nearest neighbour. Simulation study which is presented here deals with the two-class classification problem in which the considered distributions belong to the family of skewed normal distributions.
Classification : 62G30, 62H30
Keywords: data depth; classification; k-nearest neighbour; skewed normal distribution 
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     title = {$k${-Depth-nearest} {Neighbour} {Method} and its {Performance} on {Skew-normal} {Distributons}},
     journal = {Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica},
     pages = {121--129},
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     number = {2},
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     language = {en},
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Vencálek, Ondřej. $k$-Depth-nearest Neighbour Method and its Performance on Skew-normal Distributons. Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica, Tome 52 (2013) no. 2, pp. 121-129. http://geodesic.mathdoc.fr/item/AUPO_2013__52_2_a10/