A Comparative Analysis of Classification of Micro Array Gene Expression Data Using PCA

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, the paper proposed prescribed statistical procedures to efficiently perform the classification process. To further substantiate and to analyze the performance, we conduct a comparative study in this work. The comparative study with the popular dimensionality reduction technique called Principle Component Analysis (PCA The dimensionality reduction techniques replace the proposed statistical approach and perform microarray gene expression data classification. Based on the obtained results, we conduct the performance study over the combination of statistical approach with FIS, and PCA with FIS. The study results that the statistical approach with FIS outperforms the classification performance when compared to the other methods.