Genetic algorithm for entropy-based feature subset selection

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Genetic algorithm for entropy-based feature subset selection

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Genetic algorithm for entropy-based feature subset selection

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/GJ16-25694Y" target="_blank" >GJ16-25694Y: Mnohoparadigmatické algoritmy dolování z dat založené na vyhledávání, fuzzy technologiích a bio-inspirovaných výpočtech</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2016 IEEE Congress on Evolutionary Computation, CEC 2016

ISBN

978-1-5090-0622-9

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

4486-4493

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

New York

Místo konání akce

Vancouver

Datum konání akce

24. 7. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000390749104088