  Today there exists no shortage of outlier detection algorithms in the
literature, yet the complementary and critical problem of unsupervised outlier
model selection (UOMS) is vastly understudied. In this work we propose ELECT, a
new approach to select an effective candidate model, i.e. an outlier detection
algorithm and its hyperparameter(s), to employ on a new dataset without any
labels. At its core, ELECT is based on meta-learning; transferring prior
knowledge (e.g. model performance) on historical datasets that are similar to
the new one to facilitate UOMS. Uniquely, it employs a dataset similarity
measure that is performance-based, which is more direct and goal-driven than
other measures used in the past. ELECT adaptively searches for similar
historical datasets, as such, it can serve an output on-demand, being able to
accommodate varying time budgets. Extensive experiments show that ELECT
significantly outperforms a wide range of basic UOMS baselines, including no
model selection (always using the same popular model such as iForest) as well
as more recent selection strategies based on meta-features.
