Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F11%3A00365937" target="_blank" >RIV/67985556:_____/11:00365937 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition
Original language description
The paper addresses the problem of making dependency-aware feature selection feasible in pattern recognition problems of very high dimensionality. The idea of individually best ranking is generalized to evaluate the contextual quality of each feature ina series of randomly generated feature subsets. Each random subset is evaluated by a criterion function of arbitrary choice (permitting functions of high complexity). Eventually, the novel dependency-aware feature rank is computed, expressing the averagebenefit of including a feature into feature subsets. The method is efficient and generalizes well especially in very-high-dimensional problems, where traditional context-aware feature selection methods fail due to prohibitive computational complexity orto over-fitting. The method is shown well capable of over-performing the commonly applied individual ranking which ignores important contextual information contained in data.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/1M0572" target="_blank" >1M0572: Data, algorithms, decision making</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2011)
ISBN
978-1-4577-0653-0
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
502-509
Publisher name
IEEE
Place of publication
Piscataway
Event location
Anchorage, Alaska
Event date
Oct 9, 2011
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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