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 non-parametric models, 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 multi-modal class distributions. Experiments on several datasets show that the proposed approach obtains comparable and, in some cases, state-of-the-art results.

Binary Classification Using Pairs of Minimum Spanning Trees or N-Ary Trees

La Grassa R.;Gallo I.;Calefati A.;
2019

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 non-parametric models, 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 multi-modal class distributions. Experiments on several datasets show that the proposed approach obtains comparable and, in some cases, state-of-the-art results.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11383/2083808
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