DE-SVMRank: a differential evolution algorithm with a rank-based feature selection process for microarray data classification

DNA Microarrays are powerful tools to analyze and identify certain disease from the expression level of the genes in tissues samples. Many machine learning techniques are suitable for building predictive models to classify microarray samples into different biological categories. The accuracy of the...

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Autores principales: Apolloni, Javier, Leguizamón, Guillermo, Alba Torres, Enrique
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2012
Materias:
Ciencias Informáticas
Feature Selection
Support VectorMachines
Binary Differential Evolution
Ranking of Features
Algorithms
Intelligent agents
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23609
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-23609
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Feature Selection
Support VectorMachines
Binary Differential Evolution
Ranking of Features
Algorithms
Intelligent agents
spellingShingle Ciencias Informáticas
Feature Selection
Support VectorMachines
Binary Differential Evolution
Ranking of Features
Algorithms
Intelligent agents
Apolloni, Javier
Leguizamón, Guillermo
Alba Torres, Enrique
DE-SVMRank: a differential evolution algorithm with a rank-based feature selection process for microarray data classification
topic_facet Ciencias Informáticas
Feature Selection
Support VectorMachines
Binary Differential Evolution
Ranking of Features
Algorithms
Intelligent agents
description DNA Microarrays are powerful tools to analyze and identify certain disease from the expression level of the genes in tissues samples. Many machine learning techniques are suitable for building predictive models to classify microarray samples into different biological categories. The accuracy of the predictive model may benefit from a relevant feature selection method and even more, if the features are ordered in terms of its relevance. In this paper, we propose a rank-based method to create the initial population in a Binary DE-SVM based algorithm used to build a predictive model. The new algorithm (DE-SVMRank) is evaluated in terms of the achieved accuracy by the predictive model and also, the execution time required to complete the maximun number of iterations. Experimental results on public-domain microarrays show that our proposal reduces the computational time in comparison with a similar approach while providing highly competitive results.
format Objeto de conferencia
Objeto de conferencia
author Apolloni, Javier
Leguizamón, Guillermo
Alba Torres, Enrique
author_facet Apolloni, Javier
Leguizamón, Guillermo
Alba Torres, Enrique
author_sort Apolloni, Javier
title DE-SVMRank: a differential evolution algorithm with a rank-based feature selection process for microarray data classification
title_short DE-SVMRank: a differential evolution algorithm with a rank-based feature selection process for microarray data classification
title_full DE-SVMRank: a differential evolution algorithm with a rank-based feature selection process for microarray data classification
title_fullStr DE-SVMRank: a differential evolution algorithm with a rank-based feature selection process for microarray data classification
title_full_unstemmed DE-SVMRank: a differential evolution algorithm with a rank-based feature selection process for microarray data classification
title_sort de-svmrank: a differential evolution algorithm with a rank-based feature selection process for microarray data classification
publishDate 2012
url http://sedici.unlp.edu.ar/handle/10915/23609
work_keys_str_mv AT apollonijavier desvmrankadifferentialevolutionalgorithmwitharankbasedfeatureselectionprocessformicroarraydataclassification
AT leguizamonguillermo desvmrankadifferentialevolutionalgorithmwitharankbasedfeatureselectionprocessformicroarraydataclassification
AT albatorresenrique desvmrankadifferentialevolutionalgorithmwitharankbasedfeatureselectionprocessformicroarraydataclassification
bdutipo_str Repositorios
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