Boosting – An Unusual Yet Attractive Optimiser

Summary

This editorial is part of a For-Discussion- Section of Methods of Information in Medicine about the papers “The Evolution of Boosting Algorithms – From Machine Learning to Statistical Modelling” [1] and “Ex-tending Statistical Boosting – An Overview of Recent Methodological Developments” [2], written by Andreas Mayr and co authors. It preludes two discussed reviews on developments and applications of boosting in biomedical research. The two review papers, written by Andreas Mayr, Harald Binder, Olaf Gefeller, and Matthias Schmid, give an overview on recently published methods that utilise gradient or likelihood-based boosting for fitting models in the life sciences. The reviews are followed by invited comments [3] by experts in both boosting theory and applications.

• References

• 1 Mayr A, Binder H, Gefeller O, Schmid M. The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling. Methods Inf Med 2014; 53 (06) 419-427.
• 2 Mayr A, Binder H, Gefeller O, Schmid M. Extending Statistical Boosting - An Overview of Recent Methodological Developments. Methods Inf Med 2014; 53 (06) 428-435.
• 3 Bühlmann P, Gertheiss J, Hieke S, Kneib T, Ma S, Schumacher M. et al. Discussion of “The Evolution of Boosting Algorithms” and “Extending Statistical Boosting”. Methods Inf Med 2014; 53 (06) 436-445.
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