Cover Image

Map-elites algorithm for features selection problem

In the High-dimensional data analysis there are several challenges in the fields of machine learning and data mining. Typically, feature selection is considered as a combinatorial optimization problem which seeks to remove irrelevant and redundant data by reducing computation time and improve learni...

Full description

Saved in:
Main Author: Quiñonez, Brenda (author)
Other Authors: Pinto Roa, Diego Pedro (author), García Torres, Miguel (author), García-Diaz, María E. (author), Núñez Castillo, Carlos Heriberto (author), Divina, Federico (author)
Format: article
Language:English
Published: 2019
Subjects:
4. Transporte, telecomunicaciones y otras infraestructuras
FEATURE SELECTION
MAP-ELITES
COMBINATORIAL OPTIMIZATION
MACHINE LEARNING
DATA MINING
INFORMATICA
Online Access:http://hdl.handle.net/20.500.14066/3734
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1870612069465718784
author Quiñonez, Brenda
author2 Pinto Roa, Diego Pedro
García Torres, Miguel
García-Diaz, María E.
Núñez Castillo, Carlos Heriberto
Divina, Federico
author2_role author
author
author
author
author
author_browse Divina, Federico
García Torres, Miguel
García-Diaz, María E.
Núñez Castillo, Carlos Heriberto
Pinto Roa, Diego Pedro
Quiñonez, Brenda
author_facet Quiñonez, Brenda
Pinto Roa, Diego Pedro
García Torres, Miguel
García-Diaz, María E.
Núñez Castillo, Carlos Heriberto
Divina, Federico
author_role author
bitstream.checksum.fl_str_mv e7ef3f95da8387c0b26731404359eb24
858b22fda432bd774e469302988c1974
e8c4f769d64d1ad4a619b1b68e61dabc
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
bitstream.url.fl_str_mv http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3734/1/PINV15-257art2.pdf
http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3734/2/license.txt
http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3734/3/PINV15-257art2.pdf.txt
dc.contributor.other.es.fl_str_mv Universidad Nacional de Asunción - Facultad Politécnica
dc.creator.none.fl_str_mv Quiñonez, Brenda
Pinto Roa, Diego Pedro
García Torres, Miguel
García-Diaz, María E.
Núñez Castillo, Carlos Heriberto
Divina, Federico
dc.date.accessioned.none.fl_str_mv 2022-04-27T23:42:14Z
dc.date.available.none.fl_str_mv 2022-04-27T23:42:14Z
dc.date.issued.none.fl_str_mv 2019
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.14066/3734
dc.language.iso.es.fl_str_mv eng
dc.relation.projectCONACYT.es.fl_str_mv PINV15-257
dc.rights.accessRights.es.fl_str_mv info:eu-repo/semantics/openAccess
dc.subject.classification.es.fl_str_mv 4. Transporte, telecomunicaciones y otras infraestructuras
dc.subject.ocde.es.fl_str_mv INFORMATICA
dc.subject.other.es.fl_str_mv FEATURE SELECTION
MAP-ELITES
COMBINATORIAL OPTIMIZATION
MACHINE LEARNING
DATA MINING
dc.title.es.fl_str_mv Map-elites algorithm for features selection problem
dc.type.es.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
description In the High-dimensional data analysis there are several challenges in the fields of machine learning and data mining. Typically, feature selection is considered as a combinatorial optimization problem which seeks to remove irrelevant and redundant data by reducing computation time and improve learning measures. Given the complexity of this problem, we propose a novel Map-Elites based Algorithm that determines the minimum set of features maximizing learning accuracy simultaneously. Experimental results, on several data based from real scenarios, show the effectiveness of the proposed algorithm.
eu_rights_str_mv openAccess
format article
id CONACYT_87cdf61a57c09bdf85fe330a5985b9bc
language eng
network_acronym_str CONACYT
network_name_str Repositorio Institucional CONACYT
oai_identifier_str oai:repositorio.conacyt.gov.py:20.500.14066/3734
publishDate 2019
publishDateSort 2019
repository.mail.fl_str_mv repositorio.institucional@conacyt.gov.py
repository.name.fl_str_mv Repositorio Institucional CONACYT
repository_id_str
spelling 2ce2a87a-07c7-49f2-bd62-a023fa9ef3b26009026000000-0003-2479-9876386292aa-adcb-4977-a6aa-08b85eb571e060099355191-9084-4a63-9f18-d8ae9d3040ea60081160032a48873-af97-439e-bf26-eb8c2883849b600Universidad Nacional de Asunción - Facultad Politécnica2022-04-27T23:42:14Z2022-04-27T23:42:14Z2019http://hdl.handle.net/20.500.14066/3734In the High-dimensional data analysis there are several challenges in the fields of machine learning and data mining. Typically, feature selection is considered as a combinatorial optimization problem which seeks to remove irrelevant and redundant data by reducing computation time and improve learning measures. Given the complexity of this problem, we propose a novel Map-Elites based Algorithm that determines the minimum set of features maximizing learning accuracy simultaneously. Experimental results, on several data based from real scenarios, show the effectiveness of the proposed algorithm.Consejo Nacional de Ciencia y TecnologíaPROCIENCIAeng4. Transporte, telecomunicaciones y otras infraestructurasFEATURE SELECTIONMAP-ELITESCOMBINATORIAL OPTIMIZATIONMACHINE LEARNINGDATA MININGINFORMATICAMap-elites algorithm for features selection probleminfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion2019Asunción, PYInternational Workshop on Foundation of Databases and the Web (AMW 2019)15PINV15-257info:eu-repo/semantics/openAccessQuiñonez, BrendaPinto Roa, Diego PedroGarcía Torres, MiguelGarcía-Diaz, María E.Núñez Castillo, Carlos HeribertoDivina, FedericoORIGINALPINV15-257art2.pdfPINV15-257art2.pdfapplication/pdf320777http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3734/1/PINV15-257art2.pdfe7ef3f95da8387c0b26731404359eb24MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81698http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3734/2/license.txt858b22fda432bd774e469302988c1974MD52TEXTPINV15-257art2.pdf.txtPINV15-257art2.pdf.txtExtracted texttext/plain11274http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3734/3/PINV15-257art2.pdf.txte8c4f769d64d1ad4a619b1b68e61dabcMD5320.500.14066/3734oai:repositorio.conacyt.gov.py:20.500.14066/37342026-02-12 19:30:31.757Repositorio Institucional CONACYTrepositorio.institucional@conacyt.gov.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
spellingShingle Map-elites algorithm for features selection problem
Quiñonez, Brenda
4. Transporte, telecomunicaciones y otras infraestructuras
FEATURE SELECTION
MAP-ELITES
COMBINATORIAL OPTIMIZATION
MACHINE LEARNING
DATA MINING
INFORMATICA
status_str publishedVersion
title Map-elites algorithm for features selection problem
title_full Map-elites algorithm for features selection problem
title_fullStr Map-elites algorithm for features selection problem
title_full_unstemmed Map-elites algorithm for features selection problem
title_short Map-elites algorithm for features selection problem
title_sort Map-elites algorithm for features selection problem
topic 4. Transporte, telecomunicaciones y otras infraestructuras
FEATURE SELECTION
MAP-ELITES
COMBINATORIAL OPTIMIZATION
MACHINE LEARNING
DATA MINING
INFORMATICA
url http://hdl.handle.net/20.500.14066/3734

Similar Items