Optimized distance metrics for differential evolution based nearest prototype classifier

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F12%3A86085129" target="_blank" >RIV/61989100:27740/12:86085129 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Optimized distance metrics for differential evolution based nearest prototype classifier

Popis výsledku v původním jazyce

In this article, we introduce a differential evolution based classifier with extension for selecting automatically the applied distance measure from a predefined pool of alternative distances measures to suit optimally for classifying the particular dataset at hand. The proposed method extends the earlier differential evolution based nearest prototype classifier by extending the optimization process by optimizing not only the required parameters for distance measures, but also optimizing the selectionof the distance measure it self in order to find the best possible distance measure for the particular data set at hand. It has been clear for some time that in classification, usual euclidean distance is often not the best choice, and the optimal distance measure depends on the particular properties of the data sets to be classified. So far solving this issue have been subject to a limited attention in the literature. In cases where some consideration to this is problem is given, there

Název v anglickém jazyce

Optimized distance metrics for differential evolution based nearest prototype classifier

Popis výsledku anglicky

In this article, we introduce a differential evolution based classifier with extension for selecting automatically the applied distance measure from a predefined pool of alternative distances measures to suit optimally for classifying the particular dataset at hand. The proposed method extends the earlier differential evolution based nearest prototype classifier by extending the optimization process by optimizing not only the required parameters for distance measures, but also optimizing the selectionof the distance measure it self in order to find the best possible distance measure for the particular data set at hand. It has been clear for some time that in classification, usual euclidean distance is often not the best choice, and the optimal distance measure depends on the particular properties of the data sets to be classified. So far solving this issue have been subject to a limited attention in the literature. In cases where some consideration to this is problem is given, there

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

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Svazek periodika

39

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

7

Strana od-do

10564-10570

Kód UT WoS článku

000305863300024

EID výsledku v databázi Scopus

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