Binary Classification using Pairs of Minimum Spanning Trees or N-ary Trees
This work addresses binary classification problems where classes are difficult to separate due to overlapping and data imbalance, but it is incremental as it builds on existing one-class classifier methods.
The authors tackled binary classification by combining one-class classifiers based on non-parametric models like N-ary Trees and Minimum Spanning Trees, addressing issues such as classifier inconsistencies and spurious connections in multi-modal data. Their approach was shown to be feasible and comparable to state-of-the-art algorithms in tests on several datasets.
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.