A DEPTH-BASED MODIFICATION OF THE K-NEAREST NEIGHBOUR METHOD
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10435712" target="_blank" >RIV/00216208:11320/21:10435712 - isvavai.cz</a>
Alternative codes found
RIV/61989592:15310/21:73610110
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=tmT3TMNbOl" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=tmT3TMNbOl</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14736/kyb-2021-1-0015" target="_blank" >10.14736/kyb-2021-1-0015</a>
Alternative languages
Result language
angličtina
Original language name
A DEPTH-BASED MODIFICATION OF THE K-NEAREST NEIGHBOUR METHOD
Original language description
We propose a new nonparametric procedure to solve the problem of classifying objects represented by d-dimensional vectors into K >= 2 groups. The newly proposed classifier was inspired by the k nearest neighbour (kNN) method. It is based on the idea of a depth-based distributional neighbourhood and is called k nearest depth neighbours (kNDN) classifier. The kNDN classifier has several desirable properties: in contrast to the classical kNN, it can utilize global properties of the considered distributions (symmetry). In contrast to the maximal depth classifier and related classifiers, it does not have problems with classification when the considered distributions differ in dispersion or have unequal priors. The kNDN classifier is compared to several depth-based classifiers as well as the classical kNN method in a simulation study. According to the average misclassification rates, it is comparable to the best current depth-based classifiers.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/EF17_049%2F0008408" target="_blank" >EF17_049/0008408: Hydrodynamic design of pumps</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Kybernetika
ISSN
0023-5954
e-ISSN
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Volume of the periodical
57
Issue of the periodical within the volume
1
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
23
Pages from-to
15-37
UT code for WoS article
000626598800002
EID of the result in the Scopus database
2-s2.0-85103245198