Evolutionary Feature Subset Selection with Compression-based Entropy Estimation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099076" target="_blank" >RIV/61989100:27240/16:86099076 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1145/2908812.2908853" target="_blank" >http://dx.doi.org/10.1145/2908812.2908853</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/2908812.2908853" target="_blank" >10.1145/2908812.2908853</a>

Result language
angličtina
Original language name
Evolutionary Feature Subset Selection with Compression-based Entropy Estimation
Original language description
Modern massive data sets often comprise of millions of records and thousands of features. Their efficient processing by traditional methods represents an increasing challenge. Feature selection methods form a family of traditional instruments for data dimensionality reduction. They aim at selecting subsets of data features so that the loss of information, contained in the full data set, is minimized. Evolutionary feature selection methods have shown good ability to identify feature subsets in very-high-dimensional data sets. Their efficiency depends, among others, on a particular optimization algorithm, feature subset representation, and objective function definition. In this paper, two evolutionary methods for fixed-length subset selection are employed to find feature subsets on the basis of their entropy, estimated by a fast data compression algorithm. The reasonability of the fitness criterion, ability of the investigated methods to find good feature subsets, and the usefulness of selected feature subsets for practical data mining, is evaluated using two well-known data sets and several widely-used classification algorithms.
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
GECCO'16 : proceedings of the 2016 Genetic and evolutionary computation conference
ISBN
978-1-4503-4206-3
ISSN
—
e-ISSN
—
Number of pages
8
Pages from-to
933-940
Publisher name
Association for Computing Machinery
Place of publication
New York
Event location
Denver
Event date
Jul 20, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000382659200118

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