Performance Enhancement of Dimension Reduction for Microarray Data

Due to the importance of gene expression data in cancer diagnosis and treatment, microarray gene expression data have concerned more and more attentions from cancer researchers in recent years. This paper proposes and implements collision of the employ of dimension reduction methods for the microarray datasets. However, in real-world computational analysis, such data common congregate with the curse of dimensionality due to the tens of thousands of measures of data. Therefore, developing effective dimension reduction method is a tricky problem for high dimensional dataset. Here, we used two Algorithms that is Total PLS and MINE method for dimensional reduction of the microarray data. Next to this step the Hybrid algorithm is applied to the data where in Hybrid algorithm the Total PLS and MINE algorithm are combined. Overall results shown that using Hybrid algorithm provides an improvement in performance of redundancy, efficiency, Accuracy and deviation rate as compared to previous algorithms used.