Novel application of heuristic optimisation enables the creation and thorough evaluation of robust support vector machine ensembles for machine learning applications

Today’s researchers have access to an unprecedented range of powerful machine learning tools with which to build models for classifying samples according to their metabolomic profile (e.g. separating diseased samples from healthy controls). However, such powerful tools need to be used with caution and the diagnostic performance of models produced by them should be rigorously evaluated if their output is to be believed. This involves considerable processing time, and has hitherto required expert knowledge in machine learning. By adopting a constrained nonlinear simplex optimisation for the tuning of support vector machines (SVMs) we have reduced SVM training times more than tenfold compared to a traditional grid search, allowing us to implement a high performance R package that makes it possible for a typical bench scientist to produce powerful SVM ensemble classifiers within a reasonable timescale, with automated bootstrapped training and rigorous permutation testing. This puts a state-of-the-art open source multivariate classification pipeline into the hands of every metabolomics researcher, allowing them to build robust classification models with realistic performance metrics.