A Hybrid Feature Selection Optimization Model for High Dimension Data Classification

Feature selection is an NP-hard combinatorial problem, in which the number of possible feature subsets increases exponentially with features. In case large dimensionality, goal to determine smallest features considering most informative subset. this paper, we proposed a hybrid optimization model for Cancer Classification called, ENSVM. Our based on using Elastic Net (EN) method that regulates and selects variables gene genomic microarray data. We applied three different techniques namely Social Ski-Driver (SSD), Randomized SearchCV (RS) NetCV (ENCV) determining traditional Support Vector Machines classification. To evaluate model, compared results applying ENSVM seven data SSD-SVM SVM (RBF) kernel without any method. The comparison revealed effect selecting optimal subset maximized classification performance. Accordingly, minimizing significant when analyzing high dimensional performance nevertheless accuracy. Moreover, superior model.