Evolutionary Feature Subset Selection with Compression-based Entropy Estimation

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Evolutionary Feature Subset Selection with Compression-based Entropy Estimation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Evolutionary Feature Subset Selection with Compression-based Entropy Estimation

Popis výsledku anglicky

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.

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

GECCO'16 : proceedings of the 2016 Genetic and evolutionary computation conference

ISBN

978-1-4503-4206-3

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

933-940

Název nakladatele

Association for Computing Machinery

Místo vydání

New York

Místo konání akce

Denver

Datum konání akce

20. 7. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000382659200118