High dimensional data classification and feature selection using support vector machines

In many big-data systems, large amounts of information are recorded and stored for analytics purposes. Often however, this vast amount of information does not offer additional benefits for optimal decision making, but may rather be complicating and too costly for collection, storage, and processing....

ver descrição completa

Detalhes bibliográficos
Autores: Ghaddar, Bissan, Naoum-Sawaya, Joe
Formato: artículo
Fecha de publicación:2018
País:España
Recursos:IE
Repositorio:Repositorio IE
OAI Identifier:oai:repositorio.ie.edu:20.500.14417/4127
Acesso em linha:https://doi.org/10.1016/j.ejor.2017.08.040
https://hdl.handle.net/20.500.14417/4127
https://www.sciencedirect.com/science/article/abs/pii/S0377221717307713
Access Level:acceso abierto
Palavra-chave:33 Ciencias Tecnológicas::3307 Tecnología electrónica
ODS 9 - Industria, innovación e infraestructura
id ES_a1da35f63a5be5eeae27f05ff981ccdc
oai_identifier_str oai:repositorio.ie.edu:20.500.14417/4127
network_acronym_str ES
network_name_str España
repository_id_str
spelling High dimensional data classification and feature selection using support vector machinesGhaddar, BissanNaoum-Sawaya, Joe33 Ciencias Tecnológicas::3307 Tecnología electrónicaODS 9 - Industria, innovación e infraestructuraIn many big-data systems, large amounts of information are recorded and stored for analytics purposes. Often however, this vast amount of information does not offer additional benefits for optimal decision making, but may rather be complicating and too costly for collection, storage, and processing. For instance, tumor classification using high-throughput microarray data is challenging due to the presence of a large number of noisy features that do not contribute to the reduction of classification errors. For such problems, the general aim is to find a limited number of genes that highly differentiate among the classes. Thus in this paper, we address a specific class of machine learning, namely the problem of feature selection within support vector machine classification that deals with finding an accurate binary classifier that uses a minimal number of features. We introduce a new approach based on iteratively adjusting a bound on the l1-norm of the classifier vector in order to force the number of selected features to converge towards the desired maximum limit. We analyze two real-life classification problems with high dimensional features. The first case is the medical diagnosis of tumors based on microarray data where we present a generic approach for cancer classification based on gene expression. The second case deals with sentiment classification of on-line reviews from Amazon, Yelp, and IMDb. The results show that the proposed classification and feature selection approach is simple, computationally tractable, and achieves low error rates which are key for the construction of advanced decision-support systems.YesPublishedElsevierhttps://ror.org/02jjdwm7520262018info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://doi.org/10.1016/j.ejor.2017.08.040https://hdl.handle.net/20.500.14417/4127https://www.sciencedirect.com/science/article/abs/pii/S0377221717307713reponame:Repositorio IEinstname:IEInglésIE School of Science & TechnologyIE UniversityAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositorio.ie.edu:20.500.14417/41272026-06-15T12:40:57Z
dc.title.none.fl_str_mv High dimensional data classification and feature selection using support vector machines
title High dimensional data classification and feature selection using support vector machines
spellingShingle High dimensional data classification and feature selection using support vector machines
Ghaddar, Bissan
33 Ciencias Tecnológicas::3307 Tecnología electrónica
ODS 9 - Industria, innovación e infraestructura
title_short High dimensional data classification and feature selection using support vector machines
title_full High dimensional data classification and feature selection using support vector machines
title_fullStr High dimensional data classification and feature selection using support vector machines
title_full_unstemmed High dimensional data classification and feature selection using support vector machines
title_sort High dimensional data classification and feature selection using support vector machines
dc.creator.none.fl_str_mv Ghaddar, Bissan
Naoum-Sawaya, Joe
author Ghaddar, Bissan
author_facet Ghaddar, Bissan
Naoum-Sawaya, Joe
author_role author
author2 Naoum-Sawaya, Joe
author2_role author
dc.contributor.none.fl_str_mv https://ror.org/02jjdwm75
dc.subject.none.fl_str_mv 33 Ciencias Tecnológicas::3307 Tecnología electrónica
ODS 9 - Industria, innovación e infraestructura
topic 33 Ciencias Tecnológicas::3307 Tecnología electrónica
ODS 9 - Industria, innovación e infraestructura
description In many big-data systems, large amounts of information are recorded and stored for analytics purposes. Often however, this vast amount of information does not offer additional benefits for optimal decision making, but may rather be complicating and too costly for collection, storage, and processing. For instance, tumor classification using high-throughput microarray data is challenging due to the presence of a large number of noisy features that do not contribute to the reduction of classification errors. For such problems, the general aim is to find a limited number of genes that highly differentiate among the classes. Thus in this paper, we address a specific class of machine learning, namely the problem of feature selection within support vector machine classification that deals with finding an accurate binary classifier that uses a minimal number of features. We introduce a new approach based on iteratively adjusting a bound on the l1-norm of the classifier vector in order to force the number of selected features to converge towards the desired maximum limit. We analyze two real-life classification problems with high dimensional features. The first case is the medical diagnosis of tumors based on microarray data where we present a generic approach for cancer classification based on gene expression. The second case deals with sentiment classification of on-line reviews from Amazon, Yelp, and IMDb. The results show that the proposed classification and feature selection approach is simple, computationally tractable, and achieves low error rates which are key for the construction of advanced decision-support systems.
publishDate 2018
dc.date.none.fl_str_mv 2018
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.ejor.2017.08.040
https://hdl.handle.net/20.500.14417/4127
https://www.sciencedirect.com/science/article/abs/pii/S0377221717307713
url https://doi.org/10.1016/j.ejor.2017.08.040
https://hdl.handle.net/20.500.14417/4127
https://www.sciencedirect.com/science/article/abs/pii/S0377221717307713
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IE School of Science & Technology
IE University
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositorio IE
instname:IE
instname_str IE
reponame_str Repositorio IE
collection Repositorio IE
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869415213694976000
score 15.812429

Registos relacionados