Brownian distance correlation-directed search: A fast feature selection technique for alternate test

Machine-learning indirect test relies on powerful statistical algorithms to build prediction models that relate cheap measurements to costly performance metrics. Though many works in the past have been focused on proposing different models or on ways to improve the reliability of the results, it app...

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Detalhes bibliográficos
Autores: Leger, Gildas, Barragán, Manuel J.
Formato: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2016
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/194973
Acesso em linha:http://hdl.handle.net/10261/194973
Access Level:acceso cerrado
Palavra-chave:AMS-RF test
Alternate Test
Machine-learning
Feature selection
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spelling Brownian distance correlation-directed search: A fast feature selection technique for alternate testLeger, GildasBarragán, Manuel J.AMS-RF testAlternate TestMachine-learningFeature selectionMachine-learning indirect test relies on powerful statistical algorithms to build prediction models that relate cheap measurements to costly performance metrics. Though many works in the past have been focused on proposing different models or on ways to improve the reliability of the results, it appears that the main bottleneck of the approach is the definition of an information-rich input space. Finding the appropriate measurements that are both cheap and meaningful is a task that has not yet been automated. In this framework, feature selection is a necessary tool to explore possible candidates. In this paper a hybrid method is proposed that lay between filtering and wrapper-based methods, trying to strike the right balance between accuracy and speed for the particular case of Alternate Test.ElsevierConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]201920192016info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Preprintinfo:eu-repo/semantics/submittedVersionhttp://hdl.handle.net/10261/194973reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.1016/j.vlsi.2016.05.003Síinfo:eu-repo/semantics/closedAccessinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1949732026-05-22T06:33:51Z
dc.title.none.fl_str_mv Brownian distance correlation-directed search: A fast feature selection technique for alternate test
title Brownian distance correlation-directed search: A fast feature selection technique for alternate test
spellingShingle Brownian distance correlation-directed search: A fast feature selection technique for alternate test
Leger, Gildas
AMS-RF test
Alternate Test
Machine-learning
Feature selection
title_short Brownian distance correlation-directed search: A fast feature selection technique for alternate test
title_full Brownian distance correlation-directed search: A fast feature selection technique for alternate test
title_fullStr Brownian distance correlation-directed search: A fast feature selection technique for alternate test
title_full_unstemmed Brownian distance correlation-directed search: A fast feature selection technique for alternate test
title_sort Brownian distance correlation-directed search: A fast feature selection technique for alternate test
dc.creator.none.fl_str_mv Leger, Gildas
Barragán, Manuel J.
author Leger, Gildas
author_facet Leger, Gildas
Barragán, Manuel J.
author_role author
author2 Barragán, Manuel J.
author2_role author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv AMS-RF test
Alternate Test
Machine-learning
Feature selection
topic AMS-RF test
Alternate Test
Machine-learning
Feature selection
description Machine-learning indirect test relies on powerful statistical algorithms to build prediction models that relate cheap measurements to costly performance metrics. Though many works in the past have been focused on proposing different models or on ways to improve the reliability of the results, it appears that the main bottleneck of the approach is the definition of an information-rich input space. Finding the appropriate measurements that are both cheap and meaningful is a task that has not yet been automated. In this framework, feature selection is a necessary tool to explore possible candidates. In this paper a hybrid method is proposed that lay between filtering and wrapper-based methods, trying to strike the right balance between accuracy and speed for the particular case of Alternate Test.
publishDate 2016
dc.date.none.fl_str_mv 2016
2019
2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Preprint
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/194973
url http://hdl.handle.net/10261/194973
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1016/j.vlsi.2016.05.003
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/closedAccess
info:eu-repo/semantics/openAccess
eu_rights_str_mv closedAccess
openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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