Genetic algorithm for entropy-based feature subset selection

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099084" target="_blank" >RIV/61989100:27240/16:86099084 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/CEC.2016.7744360" target="_blank" >http://dx.doi.org/10.1109/CEC.2016.7744360</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CEC.2016.7744360" target="_blank" >10.1109/CEC.2016.7744360</a>

Result language
angličtina
Original language name
Genetic algorithm for entropy-based feature subset selection
Original language description
The data-driven society of today generates very large volumes of high-dimensional data. Its efficient processing by established methods represents an increasing challenge and novel advanced approaches are needed. Feature selection is a traditional data pre-processing strategy that can be used to reduce the volume and complexity of data. It selects a subset of data features so that data volume is reduced but its information content maintained. Evolutionary feature selection methods have already shown good ability to identify in very-high-dimensional data sets feature subsets according to selected criteria. Their efficiency depends, among others, on feature subset representation and objective function definition. This work employs a recent genetic algorithm for fixed-length subset selection to find feature subsets on the basis of their entropy, estimated by a fast data compression method. The reasonability of this new fitness criterion and the usefulness of selected feature subsets for practical data mining is evaluated using well-known data sets and several widely-used classification algorithms. (C) 2016 IEEE.
Czech name
—
Czech description
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Project
<a href="/en/project/GJ16-25694Y" target="_blank" >GJ16-25694Y: Multi-paradigm data mining algorithms based on information retrieval, fuzzy, and bio-inspired methods</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

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

Article name in the collection
2016 IEEE Congress on Evolutionary Computation, CEC 2016
ISBN
978-1-5090-0622-9
ISSN
—
e-ISSN
—
Number of pages
8
Pages from-to
4486-4493
Publisher name
Institute of Electrical and Electronics Engineers
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
000390749104088

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