  We provide a thorough treatment of hyperparameter optimisation for three data
descriptors with a good track-record in the literature: Support Vector Machine
(SVM), Nearest Neighbour Distance (NND) and Average Localised Proximity (ALP).
The hyperparameters of SVM have to be optimised through cross-validation, while
NND and ALP allow the reuse of a single nearest-neighbour query and an
efficient form of leave-one-out validation. We experimentally evaluate the
effect of hyperparameter optimisation with 246 classification problems drawn
from 50 datasets. From a selection of optimisation algorithms, the recent
Malherbe-Powell proposal optimises the hyperparameters of all three data
descriptors most efficiently. We calculate the increase in test AUROC and the
amount of overfitting as a function of the number of hyperparameter
evaluations. After 50 evaluations, ALP and SVM both significantly outperform
NND. The performance of ALP and SVM is comparable, but ALP can be optimised
more efficiently, while a choice between ALP and SVM based on validation AUROC
gives the best overall result. This distils the many variables of one-class
classification with hyperparameter optimisation down to a clear choice with a
known trade-off, allowing practitioners to make informed decisions.
