A DEPTH-BASED MODIFICATION OF THE K-NEAREST NEIGHBOUR METHOD

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989592:15310/21:73610110

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A DEPTH-BASED MODIFICATION OF THE K-NEAREST NEIGHBOUR METHOD

Popis výsledku v původním jazyce

We propose a new nonparametric procedure to solve the problem of classifying objects represented by d-dimensional vectors into K &gt;= 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.

Název v anglickém jazyce

A DEPTH-BASED MODIFICATION OF THE K-NEAREST NEIGHBOUR METHOD

Popis výsledku anglicky

We propose a new nonparametric procedure to solve the problem of classifying objects represented by d-dimensional vectors into K &gt;= 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/EF17_049%2F0008408" target="_blank" >EF17_049/0008408: Hydrodynamický design čerpadel</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Kybernetika

ISSN

0023-5954

e-ISSN

—

Svazek periodika

57

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

23

Strana od-do

15-37

Kód UT WoS článku

000626598800002

EID výsledku v databázi Scopus

2-s2.0-85103245198