Self-validated ensemble models for design of experiments
نویسندگان
چکیده
One of the possible objectives when designing experiments is to build or formulate a model for predicting future observations. When primary objective prediction, some typical approaches in planning phase are use well-established small-sample experimental designs design (e.g., Definitive Screening Designs) and construct predictive models using widely used selection algorithms such as LASSO. These analytic strategies, however, do not guarantee high prediction performance, partly due small sample sizes that prevent partitioning data into training validation sets, strategy commonly machine learning improve out-of-sample prediction. In this work, we propose novel framework building high-performance from capitalizes on advantage having both sets. However, instead two mutually exclusive subsets, weighting scheme based fractional random weight bootstrap emulates by assigning anti-correlated weights each observation. The proposed methodology, called Self-Validated Ensemble Modeling (SVEM), proceeds spirit bagging so it iterates through bootstraps fitted models, with final SVEM being average bootstrapped models. We investigate performance algorithm several model-building stepwise regression, Lasso, Dantzig selector. Finally, simulation case studies, show generally generates better comparison one-shot approaches.
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ژورنال
عنوان ژورنال: Chemometrics and Intelligent Laboratory Systems
سال: 2021
ISSN: ['1873-3239', '0169-7439']
DOI: https://doi.org/10.1016/j.chemolab.2021.104439