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An Efficient Ensemble Learning Method for Gene Microarray Classification

The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis....

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Autores principales: Osareh, Alireza, Shadgar, Bita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3759279/
https://www.ncbi.nlm.nih.gov/pubmed/24024194
http://dx.doi.org/10.1155/2013/478410
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author Osareh, Alireza
Shadgar, Bita
author_facet Osareh, Alireza
Shadgar, Bita
author_sort Osareh, Alireza
collection PubMed
description The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.
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spelling pubmed-37592792013-09-10 An Efficient Ensemble Learning Method for Gene Microarray Classification Osareh, Alireza Shadgar, Bita Biomed Res Int Research Article The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost. Hindawi Publishing Corporation 2013 2013-08-14 /pmc/articles/PMC3759279/ /pubmed/24024194 http://dx.doi.org/10.1155/2013/478410 Text en Copyright © 2013 A. Osareh and B. Shadgar. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Osareh, Alireza
Shadgar, Bita
An Efficient Ensemble Learning Method for Gene Microarray Classification
title An Efficient Ensemble Learning Method for Gene Microarray Classification
title_full An Efficient Ensemble Learning Method for Gene Microarray Classification
title_fullStr An Efficient Ensemble Learning Method for Gene Microarray Classification
title_full_unstemmed An Efficient Ensemble Learning Method for Gene Microarray Classification
title_short An Efficient Ensemble Learning Method for Gene Microarray Classification
title_sort efficient ensemble learning method for gene microarray classification
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3759279/
https://www.ncbi.nlm.nih.gov/pubmed/24024194
http://dx.doi.org/10.1155/2013/478410
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