Hybrid Dimensionality Reduction for Classification of Microarray Data
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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 va...
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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