OFMDC: Optimal Framework for Microarray Data Classification Using Eigenvector Decomposition for Cancer Disease

Abstract

In the recent era, the research interest has increased among different computer and communication societies towards microarray gene expression detection and profiling. Despite of having a wide range of applications, the more emphasize has been kept towards cancer and its sub-type classifications. It has been seen in the past that the existing data mining approaches impose more cost of computation during pattern discovery and correlation establishment. Thereby, it is needed to address this shortcoming to strengthen the reliable cancer detection and classification process cost-effectively. An efficient machine learning tool has a better scope of optimization towards handling margin and error factors. Addressing this open research issue, the current study has come up with a novel method namely Optimal Framework for Microarray Data Classification (OFDMC) which incorporates Eigenvector decomposition to perform dimension reduction of gene expression data without compromising the complexity and accuracy aspects. The study also validates the performance of the proposed system by introducing a numerical analysis.