Hybrid Dimensionality Reduction for Classification of Microarray Data - Couverture souple

Arowolo, Micheal; Gbolagade, Kazeem; Abdulsalam, Sulaiman

 
9786202064330: Hybrid Dimensionality Reduction for Classification of Microarray Data
Couverture souple
ISBN 10 : 6202064331 ISBN 13 : 9786202064330
Editeur : LAP LAMBERT Academic Publishing, 2017

Synopsis

High dimensionality affects the performance of classifiers, especially for microarray gene expression data sets. Many efficient dimensionality reduction techniques that transform these high dimensional data into a reduced form have been proposed for microarray data analysis. These techniques perform well. However, these techniques need to be improved in systematic ways as regards to their performance metrics. This study combines the two dimensionality reduction technique, feature selection and feature extraction, to address the problems of highly correlated data and selection of significant variables out of a set of features, by assessing important and significant dimensionality reduction techniques contributing to efficient classification of genes in a data. One-Way-ANOVA is employed for feature selection to obtain an optimal number of genes; Principal Component Analysis (PCA) as well as Partial Least Squares (PLS) is employed as feature extraction methods separately, to reduce the selected features from microarray dataset. An experimental result on colon cancer dataset uses Support Vector Machine (SVM) as a classifier.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

High dimensionality affects the performance of classifiers, especially for microarray gene expression data sets. Many efficient dimensionality reduction techniques that transform these high dimensional data into a reduced form have been proposed for microarray data analysis. These techniques perform well. However, these techniques need to be improved in systematic ways as regards to their performance metrics. This study combines the two dimensionality reduction technique, feature selection and feature extraction, to address the problems of highly correlated data and selection of significant variables out of a set of features, by assessing important and significant dimensionality reduction techniques contributing to efficient classification of genes in a data. One-Way-ANOVA is employed for feature selection to obtain an optimal number of genes; Principal Component Analysis (PCA) as well as Partial Least Squares (PLS) is employed as feature extraction methods separately, to reduce the selected features from microarray dataset. An experimental result on colon cancer dataset uses Support Vector Machine (SVM) as a classifier.

Biographie de l'auteur

Arowolo Micheal Olaolu holds a B.Sc in Computer Science from Al-Hikmah University, Ilorin, Nigeria, and M.Sc in Computer Science from Kwara state University, Malete, Nigeria. He is an Oracle Certified Expert, Member IAENG and SDIWC. His research interest includes Bio-informatics, Datamining and parallel computing.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2017
Langue
anglais
ISBN 10 
6202064331
ISBN 13 
9786202064330
Reliure
Broché
Nombre de pages
116
Coordonnées du fabricant
non disponible
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