Optimization of the ANOVA Procedure for Support Vector Machines

Abstract

Feature selection is a powerful tool to identify the important characteristics of data for prediction. Feature selection, therefore, can be a tool for avoiding overfitting, improving prediction accuracy and reducing execution time. The applications of feature selection procedures are particularly important in Support vector machines, which is used for prediction in large datasets. The larger the dataset, the more computationally exhaustive and challenging it is to build a predictive model using the support vector classifier. This paper investigates how the feature selection approach based on the analysis of variance (ANOVA) can be optimized for Support Vector Machines (SVMs) to improve its execution time and accuracy. We introduce new conditions on the SVMs prior to running the ANOVA to optimize the performance of the support vector classifier. We also establish the bootstrap procedure as alternative to cross validation to perform model selection. We run our experiments using popular datasets and compare our results to existing modifications of SVMs with feature selection procedure. We propose a number of ANOVA-SVM modifications which are simple to perform, while at the same time, boost significantly the accuracy and computing time of the SVMs in comparison to existing methods like the Mixed Integer Linear Feature Selection approach.