Improving Sequential Feature Selection Methods Performance by Means of Hybridization

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F10%3A00341554" target="_blank" >RIV/67985556:_____/10:00341554 - isvavai.cz</a>
Alternative codes found
RIV/61384399:31160/10:00036188
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Improving Sequential Feature Selection Methods Performance by Means of Hybridization
Original language description
In this paper we propose the general scheme of defining hybrid feature selection algorithms based on standard sequential search with the aim to improve feature selection performance, especially on high-dimensional or large-sample data. We show experimentally that ?hybridization has not only the potential to dramatically reduce FS search time, but in some cases also to actually improve classifier generalization, i.e., its classification performance on previously unknown data.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proc. 6th IASTED Int. Conf. on Advances in Computer Science and Engineering
ISBN
978-0-88986-830-4
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
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Publisher name
ACTA Press
Place of publication
Calgary
Event location
Sharm El Sheikh
Event date
Mar 15, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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