Parameters Optimization of Elastic NET for High Dimensional Data using PSO Algorithm

The feature selection method is regarded as an issue with the global combinatorial optimization technique, which aims to reduce the number of features, eliminate irrelevant, noisy and redundant data, such as microarray cancer data containing a small number of samples that have a large number of gene expression levels as features. To select the optimal subset of gene and reduce the dimensionality of cancer microarray data to improve the performance of the classification accuracy. This paper presents a model called PSO-ENSVM which is a hybrid between feature selection, optimization and classification methods. We use a Swarm optimization PSO algorithm which it's mainly the objective of this research is to have space to get near-optimal, optimal or solutions for optimizing the tuning parameters of Elastic Net and SVM as a classifier. To evaluate the model, we use seven microarray data sets for different cancer type, and we compared the PSO-ENSVM model with the PSO-SVM a model that optimizes RBF Kernel hyperparameter without feature selection and SVM with RBF Kernel. The experimental results were presented and showed that the ability of our model to obtain an ideal subset of the feature led to increased rates performance as it was able to reduce the number of features specified. As a result, the results show that the PSO-ENSVM model is superior compared to PSO-SVM and SVM with RBF kernel.