A Scalable Memetic Algorithm for Simultaneous Instance and Feature Selection

Instance selection is becoming increasingly relevant due to the huge amount of data that is constantly produced in many fields of research. At the same time, most of the recent pattern recognition problems involve highly complex datasets with a large number of possible explanatory variables. For man...

Descripción completa

Detalles Bibliográficos
Autores: García-Pedrajas, Nicolás, Haro-García, Aida de, Pérez Rodriguez, Javier
Tipo de recurso: artículo
Fecha de publicación:2013
País:España
Institución:Universidad Loyola Andalucía
Repositorio:Brújula
OAI Identifier:oai:repositorio.uloyola.es:20.500.12412/5469
Acceso en línea:https://hdl.handle.net/20.500.12412/5469
Access Level:acceso abierto
Palabra clave:Memetic algorithms
Instance selection
Feature selection
Scaling-up
id ES_2a44fcaf411c898ecc6af3c14671f921
oai_identifier_str oai:repositorio.uloyola.es:20.500.12412/5469
network_acronym_str ES
network_name_str España
repository_id_str
spelling A Scalable Memetic Algorithm for Simultaneous Instance and Feature SelectionGarcía-Pedrajas, NicolásHaro-García, Aida dePérez Rodriguez, JavierMemetic algorithmsInstance selectionFeature selectionScaling-upInstance selection is becoming increasingly relevant due to the huge amount of data that is constantly produced in many fields of research. At the same time, most of the recent pattern recognition problems involve highly complex datasets with a large number of possible explanatory variables. For many reasons, this abundance of variables significantly harms classification or recognition tasks. There are efficiency issues, too, because the speed of many classification algorithms is largely improved when the complexity of the data is reduced. One of the approaches to address problems that have too many features or instances is feature or instance selection, respectively. Although most methods address instance and feature selection separately, both problems are interwoven, and benefits are expected from facing these two tasks jointly. This paper proposes a new memetic algorithm for dealing with many instances and many features simultaneously by performing joint instance and feature selection. The proposed method performs four different local search procedures with the aim of obtaining the most relevant subsets of instances and features to perform an accurate classification. A new fitness function is also proposed that enforces instance selection but avoids putting too much pressure on removing features. We prove experimentally that this fitness function improves the results in terms of testing error. Regarding the scalability of the method, an extension of the stratification approach is developed for simultaneous instance and feature selection. This extension allows the application of the proposed algorithm to large datasets. An extensive comparison using 55 medium to large datasets from the UCI Machine Learning Repository shows the usefulness of our method. Additionally, the method is applied to 30 large problems, with very good results. The accuracy of the method for classimbalanced problems in a set of 40 datasets is shown. The usefulness of the method is also tested using decision trees and support vector machines as classification methods.2013info:eu-repo/semantics/articlehttps://hdl.handle.net/20.500.12412/5469reponame:Brújulainstname:Universidad Loyola AndalucíaInglésThis work was supported in part by the Project TIN2011-22967 of the Spanish Ministry of Science and Innovation and the project P09-TIC-4623 of the Junta de Andalucíahttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositorio.uloyola.es:20.500.12412/54692026-06-24T12:48:37Z
dc.title.none.fl_str_mv A Scalable Memetic Algorithm for Simultaneous Instance and Feature Selection
title A Scalable Memetic Algorithm for Simultaneous Instance and Feature Selection
spellingShingle A Scalable Memetic Algorithm for Simultaneous Instance and Feature Selection
García-Pedrajas, Nicolás
Memetic algorithms
Instance selection
Feature selection
Scaling-up
title_short A Scalable Memetic Algorithm for Simultaneous Instance and Feature Selection
title_full A Scalable Memetic Algorithm for Simultaneous Instance and Feature Selection
title_fullStr A Scalable Memetic Algorithm for Simultaneous Instance and Feature Selection
title_full_unstemmed A Scalable Memetic Algorithm for Simultaneous Instance and Feature Selection
title_sort A Scalable Memetic Algorithm for Simultaneous Instance and Feature Selection
dc.creator.none.fl_str_mv García-Pedrajas, Nicolás
Haro-García, Aida de
Pérez Rodriguez, Javier
author García-Pedrajas, Nicolás
author_facet García-Pedrajas, Nicolás
Haro-García, Aida de
Pérez Rodriguez, Javier
author_role author
author2 Haro-García, Aida de
Pérez Rodriguez, Javier
author2_role author
author
dc.subject.none.fl_str_mv Memetic algorithms
Instance selection
Feature selection
Scaling-up
topic Memetic algorithms
Instance selection
Feature selection
Scaling-up
description Instance selection is becoming increasingly relevant due to the huge amount of data that is constantly produced in many fields of research. At the same time, most of the recent pattern recognition problems involve highly complex datasets with a large number of possible explanatory variables. For many reasons, this abundance of variables significantly harms classification or recognition tasks. There are efficiency issues, too, because the speed of many classification algorithms is largely improved when the complexity of the data is reduced. One of the approaches to address problems that have too many features or instances is feature or instance selection, respectively. Although most methods address instance and feature selection separately, both problems are interwoven, and benefits are expected from facing these two tasks jointly. This paper proposes a new memetic algorithm for dealing with many instances and many features simultaneously by performing joint instance and feature selection. The proposed method performs four different local search procedures with the aim of obtaining the most relevant subsets of instances and features to perform an accurate classification. A new fitness function is also proposed that enforces instance selection but avoids putting too much pressure on removing features. We prove experimentally that this fitness function improves the results in terms of testing error. Regarding the scalability of the method, an extension of the stratification approach is developed for simultaneous instance and feature selection. This extension allows the application of the proposed algorithm to large datasets. An extensive comparison using 55 medium to large datasets from the UCI Machine Learning Repository shows the usefulness of our method. Additionally, the method is applied to 30 large problems, with very good results. The accuracy of the method for classimbalanced problems in a set of 40 datasets is shown. The usefulness of the method is also tested using decision trees and support vector machines as classification methods.
publishDate 2013
dc.date.none.fl_str_mv 2013
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.12412/5469
url https://hdl.handle.net/20.500.12412/5469
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv This work was supported in part by the Project TIN2011-22967 of the Spanish Ministry of Science and Innovation and the project P09-TIC-4623 of the Junta de Andalucía
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Brújula
instname:Universidad Loyola Andalucía
instname_str Universidad Loyola Andalucía
reponame_str Brújula
collection Brújula
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869405058769092608
score 15,812429

Similares en LA Referencia