Hybrid Dimensionality Reduction for Classification of Microarray Data - Tapa blanda

Arowolo, Micheal; Gbolagade, Kazeem; Abdulsalam, Sulaiman

 
9786202064330: Hybrid Dimensionality Reduction for Classification of Microarray Data
Tapa blanda
ISBN 10: 6202064331 ISBN 13: 9786202064330
Editorial: LAP LAMBERT Academic Publishing, 2017

Sinopsis

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.

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Reseña del editor

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.

Biografía del autor

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.

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Editorial
LAP LAMBERT Academic Publishing
Año de publicación
2017
Idioma
Inglés
ISBN 10 
6202064331
ISBN 13 
9786202064330
Encuadernación
Tapa blanda
Número de páginas
116
Contacto del fabricante
no disponible
Persona responsable
no disponible