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A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data

Feature (gene) selection and classification of microarray data are the two most interesting machine learning challenges. In the present work two existing feature selection/extraction algorithms, namely independent component analysis (ICA) and fuzzy backward feature elimination (FBFE) are used which...

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Autores principales: Aziz, Rabia, Verma, C.K., Srivastava, Namita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4818349/
https://www.ncbi.nlm.nih.gov/pubmed/27081632
http://dx.doi.org/10.1016/j.gdata.2016.02.012
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author Aziz, Rabia
Verma, C.K.
Srivastava, Namita
author_facet Aziz, Rabia
Verma, C.K.
Srivastava, Namita
author_sort Aziz, Rabia
collection PubMed
description Feature (gene) selection and classification of microarray data are the two most interesting machine learning challenges. In the present work two existing feature selection/extraction algorithms, namely independent component analysis (ICA) and fuzzy backward feature elimination (FBFE) are used which is a new combination of selection/extraction. The main objective of this paper is to select the independent components of the DNA microarray data using FBFE to improve the performance of support vector machine (SVM) and Naïve Bayes (NB) classifier, while making the computational expenses affordable. To show the validity of the proposed method, it is applied to reduce the number of genes for five DNA microarray datasets namely; colon cancer, acute leukemia, prostate cancer, lung cancer II, and high-grade glioma. Now these datasets are then classified using SVM and NB classifiers. Experimental results on these five microarray datasets demonstrate that gene selected by proposed approach, effectively improve the performance of SVM and NB classifiers in terms of classification accuracy. We compare our proposed method with principal component analysis (PCA) as a standard extraction algorithm and find that the proposed method can obtain better classification accuracy, using SVM and NB classifiers with a smaller number of selected genes than the PCA. The curve between the average error rate and number of genes with each dataset represents the selection of required number of genes for the highest accuracy with our proposed method for both the classifiers. ROC shows best subset of genes for both the classifier of different datasets with propose method.
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spelling pubmed-48183492016-04-14 A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data Aziz, Rabia Verma, C.K. Srivastava, Namita Genom Data Regular Article Feature (gene) selection and classification of microarray data are the two most interesting machine learning challenges. In the present work two existing feature selection/extraction algorithms, namely independent component analysis (ICA) and fuzzy backward feature elimination (FBFE) are used which is a new combination of selection/extraction. The main objective of this paper is to select the independent components of the DNA microarray data using FBFE to improve the performance of support vector machine (SVM) and Naïve Bayes (NB) classifier, while making the computational expenses affordable. To show the validity of the proposed method, it is applied to reduce the number of genes for five DNA microarray datasets namely; colon cancer, acute leukemia, prostate cancer, lung cancer II, and high-grade glioma. Now these datasets are then classified using SVM and NB classifiers. Experimental results on these five microarray datasets demonstrate that gene selected by proposed approach, effectively improve the performance of SVM and NB classifiers in terms of classification accuracy. We compare our proposed method with principal component analysis (PCA) as a standard extraction algorithm and find that the proposed method can obtain better classification accuracy, using SVM and NB classifiers with a smaller number of selected genes than the PCA. The curve between the average error rate and number of genes with each dataset represents the selection of required number of genes for the highest accuracy with our proposed method for both the classifiers. ROC shows best subset of genes for both the classifier of different datasets with propose method. Elsevier 2016-02-23 /pmc/articles/PMC4818349/ /pubmed/27081632 http://dx.doi.org/10.1016/j.gdata.2016.02.012 Text en © 2016 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Regular Article
Aziz, Rabia
Verma, C.K.
Srivastava, Namita
A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data
title A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data
title_full A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data
title_fullStr A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data
title_full_unstemmed A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data
title_short A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data
title_sort fuzzy based feature selection from independent component subspace for machine learning classification of microarray data
topic Regular Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4818349/
https://www.ncbi.nlm.nih.gov/pubmed/27081632
http://dx.doi.org/10.1016/j.gdata.2016.02.012
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