Optimal Feature Selection Using GLFES and PSO in Imbalanced Microarray Dataset

In recent years the Biological mining has incurred the massive effort to identify the uncovered genetic variations that are associated with high-dimensional micro array data. The Imbalanced Micro array data analysis faces two complications for prediction. The first and the foremost problem is the unequal distribution of classes and the second issue is relevant feature selection and classificationThe primary task in the proposed work is derived to generate the initial population by selecting the relevant features which is accomplished by Granularity learning fuzzy evolutionary sampling (GLFES). The secondary task in the proposed work is associated with optimal feature selection through PSO optimization technique. Finally the selected optimal features are classified using SVM classification.