An Approach for Optimal Feature Selection in Machine Learning using Global Sensitivity Analysis

Abstract

The classification application is an important procedure for selecting the feature. The classification is mainly based on the features extracted from the object. You can select the best feature using the following three methods: wrapper selection, filter and embedded procedure. All three practices have been implemented by single or combined two approaches. As a result, there is no important feature in the classification process. This problem is solved by the proposed integrated global analysis of sensitivity. Each feature is selected in a classification based on the sensitivity of the feature and the correlation from the target vector in this integrated sensitivity and correlation approach. Likewise, the GSA approach uses a variety of filtering techniques for ranking attributes and optimization using particle swarm technique. Then, the optimum attributes are trained and tested using the Random Forest Classifier grid search via MATLAB software. In comparison to the existing method, wrapper-based selection, the performance of our integrated model is measured using sensitivity, specificity and accuracy. The experimental results of our proposed approach outweigh the sensitivities by 93.72%, 94.74% and the accuracy of 89.921% and 90% where, wrapper selection approach as sensitivity by 89.83% and the accuracy of 93%. Keywords—Feature selection; feature sensitivity; feature correlation; global sensitivity analysis; classification