Classification of Micro Array Gene Expression Proposed using Statistical Approaches

Abstract

Classification analysis of microarray gene expression data has been performed widely 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. 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 considers two popular dimensionality reduction techniques called Principle Component Analysis (PCA) and Multi-linear Principle Component Analysis (MPCA). 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, LPP with FIS and MPCA with FIS. The study results that the statistical approach with FIS outperforms the classification performance when compared to the other methods.