Feature selection via competitive levy flights

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F16%3A00305214" target="_blank" >RIV/68407700:21220/16:00305214 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21340/16:00305214 RIV/68407700:21460/16:00305214
Result on the web
<a href="http://ieeexplore.ieee.org/document/7727680/" target="_blank" >http://ieeexplore.ieee.org/document/7727680/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IJCNN.2016.7727680" target="_blank" >10.1109/IJCNN.2016.7727680</a>

Result language
angličtina
Original language name
Feature selection via competitive levy flights
Original language description
Evolutionary meta-heuristics are designed for optimization using population with selection and mutation operators. Novelty of our approach is based on competition of various operators from mutation portfolio. Resulting meta-heuristic is successfully tested on the feature selection task: searching for a sparse sub-model having the best possible value by means of information criteria.
Czech name
—
Czech description
—

Project
—
Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of International Joint Conference on Neural Networks 2016
ISBN
9781509006199
ISSN
2161-4393
e-ISSN
—
Number of pages
6
Pages from-to
3731-3736
Publisher name
IEEE
Place of publication
New York
Event location
Vancouver
Event date
Jul 24, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000399925503127

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