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Several methods for analyzing SSDs have been proposed in recent literature. While most of the literature on SSDs has focused on main effects models, the analysis of such designs involving models with interactions has not been developed to a great extent. In this paper, we attempt to relate several penalty and loss functions with support vector machines, with one main goal in mind, screening active effects in SSDs. In this spirit, we propose a penalized wrapper screening method for identifying in one stage the important main effects and two\u2010factor interactions of two\u2010level SSDs, by assuming generalized linear models. We also carry out simulation studies and a real data analysis to assess the performance of the proposed screening procedure, showing that the proposed method works satisfactorily. 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