Abstract. One-class classifiers are trained only with target class samples. Intuitively, their conservative modeling 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 leveraging on the combination of one-class classifiers based on nonparametric models, N-ary Trees and Minimum Spanning Trees class descriptors (MST CD) are proposed. These methods deal with inconsistencies arising from combining multiple classifiers and with spurious connections that MST-CD creates in multimodal class distributions. Experiments on several datasets show that the proposed approach obtains comparable and, in some cases, state-of-theart results.
In REF , La Grassa et al., creates a binary model based on the combination of two one-class classifiers.
Binary Classification using Pairs of Minimum Spanning Trees or N-ary Trees
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