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.
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About the Author
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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Hybrid Dimensionality Reduction for Classification of Microarray Data
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 116 pp. Englisch. Seller Inventory # 9786202064330
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 116 pp. Englisch. Seller Inventory # 9786202064330
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9786202064330
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