Premium
A Penalized Wrapper Method for Screening Main Effects and Interactions in Supersaturated Designs
Author(s) -
Koukouvinos C.,
Parpoula C.
Publication year - 2015
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1679
Subject(s) - computer science , fractional factorial design , r package , class (philosophy) , factorial experiment , machine learning , artificial intelligence , computational science
Supersaturated designs (SSDs) are defined as fractional factorial designs whose experimental run size is smaller than the number of main effects to be estimated. The main goal using the class of SSDs is to identify the important effects efficiently, that is, at a minimal computational cost and time. 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‐factor interactions of two‐level 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. Copyright © 2014 John Wiley & Sons, Ltd.