  One-class classifiers are trained with target class only samples.
Intuitively, their conservative modelling of the class description may benefit
classical classification tasks where classes are difficult to separate due to
overlapping and data imbalance. In this work, three methods are proposed which
leverage on the combination of one-class classifiers based on non-parametric
models, N-ary Trees and Minimum Spanning Trees class descriptors (MST-CD), to
tackle binary classification problems. The methods deal with the
inconsistencies arising from combining multiple classifiers and with spurious
connections that MST-CD creates in multi-modal class distributions. As shown by
our tests on several datasets, the proposed approach is feasible and comparable
with state-of-the-art algorithms.
