Differential Evolution Classifier with Optimized Distance Measures from a Pool of Distances

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F12%3A86085128" target="_blank" >RIV/61989100:27740/12:86085128 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/CEC.2012.6252889" target="_blank" >http://dx.doi.org/10.1109/CEC.2012.6252889</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CEC.2012.6252889" target="_blank" >10.1109/CEC.2012.6252889</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Differential Evolution Classifier with Optimized Distance Measures from a Pool of Distances

Popis výsledku v původním jazyce

In this article we propose a differential evolution based nearest prototype classifier with extension to selecting the applied distance measure from a pool of alternative measures optimally for the particular data set at hand. The proposed method extendsthe earlier differential evolution based nearest prototype classifier by extending the optimization process to cover also the selection of distance measure instead of optimizing only the parameters related with a preselected and fixed distance measure.Now the optimization process is seeking also for the best distance measure providing the highest classification accuracy over the selected data set. It has been clear for some time that in classification, the usual euclidean distance measure is sometimesnot the best possible choice. Still usually not much has been done for it, and in many cases where some consideration to this problem is given, there has only been testing with a couple of alternative distance measures to find which one

Název v anglickém jazyce

Differential Evolution Classifier with Optimized Distance Measures from a Pool of Distances

Popis výsledku anglicky

In this article we propose a differential evolution based nearest prototype classifier with extension to selecting the applied distance measure from a pool of alternative measures optimally for the particular data set at hand. The proposed method extendsthe earlier differential evolution based nearest prototype classifier by extending the optimization process to cover also the selection of distance measure instead of optimizing only the parameters related with a preselected and fixed distance measure.Now the optimization process is seeking also for the best distance measure providing the highest classification accuracy over the selected data set. It has been clear for some time that in classification, the usual euclidean distance measure is sometimesnot the best possible choice. Still usually not much has been done for it, and in many cases where some consideration to this problem is given, there has only been testing with a couple of alternative distance measures to find which one

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

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 statě ve sborníku

2012 IEEE Congress on Evolutionary Computation (CEC), 2012

ISBN

978-1-4673-1509-8

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

1-7

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Brisbane

Datum konání akce

10. 6. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000312859300034