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A Comparative Analysis of classification of Micro Array Gene Expression Data using Dimensionality Reduction Techniques

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Author(s): TamilSelvi Madeswaran | G.M.Kadhar Nawaz

Journal: International Journal of Computer & Electronics Research
ISSN 2320-9348

Volume: 1;
Issue: 4;
Start page: 192;
Date: 2012;
Original page

Keywords: Dimensionality Reduction | Microarray Gene | Feature Selection | Gene Classification | Principle Component Analysis | Multi-linear Principle Component Analysis | FIS

ABSTRACT
Cancer classification is one of the major applications of microarray technology. When standard machine learning algorithms are applied for cancer classification they face problem in the gene expression data. The problem is high dimensional dataset. Dimension reduction is a critical issue in the analysis of microarray data, because the high dimensionality of gene expression microarray data set hurts generalization performance of classifiers. Classification analysis of microarray gene expression data has been performed extensively to find out the biological features and to differentiate intimately related cell types that usually appear in the diagnosis of cancer. Many algorithms and techniques have been developed for the microarray gene classification process and dimensionality reduction of dataset. These developed techniques accomplish microarray gene classification process with the aid of three basic phases namely, dimensionality reduction, feature selection and gene classification.  In our previous work, microarray gene classification by statistical analysis approach with Fuzzy Inference System (FIS) was proposed for precise classification of genes to their corresponding gene types. Among various dimensionality reduction techniques, this paper proposed prescribed popular dimensionality reduction techniques called Principle Component Analysis (PCA) and Multi-linear Principle Component Analysis (MPCA) and perform microarray gene expression data classification. To further substantiate and to analyze the performance, we conduct a comparative study in this work.
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