A New Variable Selection Method Based on SVM for Analyzing Supersaturated Designs

Supersaturated designs (SSDs) are designs whose factors exceeds run size; thus, there are not enough runs for estimating all the main effects. They are commonly used in screening experiments, where the primary goal is to identify the few, but dominant, active factors, keeping the cost as low as possible. The development of new statistical methods inspired by machine learning algorithms is increasing rapidly, especially nowadays. One of such methods is the support vector machine recursive feature elimination (SVM-RFE), which manages to extract the informative genes in classification problems, while it achieves extremely high performance. In this article, we study a variable selection method for regression problems, called SVR-RFE, to screen active effects in both two-level and mixed-level designs. Simulation studies demonstrate that this procedure is effective enough, especially in terms of statistical power.