Variable selection and validation in multivariate modelling
Journal article, 2019

Motivation Validation of variable selection and predictive performance is crucial in construction of robust multivariate models that generalize well, minimize overfitting and facilitate interpretation of results. Inappropriate variable selection leads instead to selection bias, thereby increasing the risk of model overfitting and false positive discoveries. Although several algorithms exist to identify a minimal set of most informative variables (i.e. the minimal-optimal problem), few can select all variables related to the research question (i.e. the all-relevant problem). Robust algorithms combining identification of both minimal-optimal and all-relevant variables with proper cross-validation are urgently needed. Results We developed the MUVR algorithm to improve predictive performance and minimize overfitting and false positives in multivariate analysis. In the MUVR algorithm, minimal variable selection is achieved by performing recursive variable elimination in a repeated double cross-validation (rdCV) procedure. The algorithm supports partial least squares and random forest modelling, and simultaneously identifies minimal-optimal and all-relevant variable sets for regression, classification and multilevel analyses. Using three authentic omics datasets, MUVR yielded parsimonious models with minimal overfitting and improved model performance compared with state-of-the-art rdCV. Moreover, MUVR showed advantages over other variable selection algorithms, i.e. Boruta and VSURF, including simultaneous variable selection and validation scheme and wider applicability.

Author

Lin Shi

Chalmers, Biology and Biological Engineering, Food and Nutrition Science

Swedish University of Agricultural Sciences (SLU)

Johan A. Westerhuis

Swammerdam Institute for Life Sciences

North-West University

Johan Rosén

Swedish National Food Agency

Rikard Landberg

Chalmers, Biology and Biological Engineering, Food and Nutrition Science

Swedish University of Agricultural Sciences (SLU)

Carl Brunius

Chalmers, Biology and Biological Engineering, Food and Nutrition Science

Bioinformatics

1367-4803 (ISSN) 1367-4811 (eISSN)

Vol. 35 6 972-980

Subject Categories

Bioinformatics (Computational Biology)

Probability Theory and Statistics

Control Engineering

DOI

10.1093/bioinformatics/bty710

PubMed

30165467

More information

Latest update

5/7/2019 1