Feature selection for chemical sensor arrays using mutual information.

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best featur...

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Autores principales:	X Rosalind Wang, Joseph T Lizier, Thomas Nowotny, Amalia Z Berna, Mikhail Prokopenko, Stephen C Trowell
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/65b88b8dddd34bd7bddccbc9fcf82e7a
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spelling	oai:doaj.org-article:65b88b8dddd34bd7bddccbc9fcf82e7a2021-11-18T08:29:49ZFeature selection for chemical sensor arrays using mutual information.1932-620310.1371/journal.pone.0089840https://doaj.org/article/65b88b8dddd34bd7bddccbc9fcf82e7a2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24595058/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays.X Rosalind WangJoseph T LizierThomas NowotnyAmalia Z BernaMikhail ProkopenkoStephen C TrowellPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 3, p e89840 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q X Rosalind Wang Joseph T Lizier Thomas Nowotny Amalia Z Berna Mikhail Prokopenko Stephen C Trowell Feature selection for chemical sensor arrays using mutual information.
description	We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays.
format	article
author	X Rosalind Wang Joseph T Lizier Thomas Nowotny Amalia Z Berna Mikhail Prokopenko Stephen C Trowell
author_facet	X Rosalind Wang Joseph T Lizier Thomas Nowotny Amalia Z Berna Mikhail Prokopenko Stephen C Trowell
author_sort	X Rosalind Wang
title	Feature selection for chemical sensor arrays using mutual information.
title_short	Feature selection for chemical sensor arrays using mutual information.
title_full	Feature selection for chemical sensor arrays using mutual information.
title_fullStr	Feature selection for chemical sensor arrays using mutual information.
title_full_unstemmed	Feature selection for chemical sensor arrays using mutual information.
title_sort	feature selection for chemical sensor arrays using mutual information.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/65b88b8dddd34bd7bddccbc9fcf82e7a
work_keys_str_mv	AT xrosalindwang featureselectionforchemicalsensorarraysusingmutualinformation AT josephtlizier featureselectionforchemicalsensorarraysusingmutualinformation AT thomasnowotny featureselectionforchemicalsensorarraysusingmutualinformation AT amaliazberna featureselectionforchemicalsensorarraysusingmutualinformation AT mikhailprokopenko featureselectionforchemicalsensorarraysusingmutualinformation AT stephenctrowell featureselectionforchemicalsensorarraysusingmutualinformation
_version_	1718421744454205440

Feature selection for chemical sensor arrays using mutual information.

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