Optimized distance metrics for differential evolution based nearest prototype classifier

Result code in 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>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Optimized distance metrics for differential evolution based nearest prototype classifier
Original language description
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
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Project
<a href="/en/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: IT4Innovations Centre of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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