Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized
  Controlled Trials?
Walter Nelson Jonathan Ranisau Jeremy Petch
Modern multi-centre randomized controlled trials (MCRCTs) collect massive
amounts of tabular data, and are monitored intensively for irregularities by
humans. We began by empirically evaluating 6 modern machine learning-based
outlier detection algorithms on the task of identifying irregular data in 838
datasets from 7 real-world MCRCTs with a total of 77,001 patients from over 44
countries. Our results reinforce key findings from prior work in the outlier
detection literature on data from other domains. Existing algorithms often
succeed at identifying irregularities without any supervision, with at least
one algorithm exhibiting positive performance 70.6% of the time. However,
performance across datasets varies substantially with no single algorithm
performing consistently well, motivating new techniques for unsupervised model
selection or other means of aggregating potentially discordant predictions from
multiple candidate models. We propose the Meta-learned Probabilistic Ensemble
(MePE), a simple algorithm for aggregating the predictions of multiple
unsupervised models, and show that it performs favourably compared to recent
meta-learning approaches for outlier detection model selection. While
meta-learning shows promise, small ensembles outperform all forms of
meta-learning on average, a negative result that may guide the application of
current outlier detection approaches in healthcare and other real-world
domains.