Personalized mode transductive spanning SVM classification tree

Personalized transductive learning (PTL) builds a unique local model for classification of individual test samples and is therefore practically neighborhood dependant; i.e. a specific model is built in a subspace spanned by a set of samples adjacent to the test sample. While existing PTL methods usually define the neighborhood by a predefined (dis)similarity measure, this paper introduces a new concept of a knowledgeable neighborhood and a transductive Support Vector Machine (SVM) classification tree (t-SVMT) for PTL. The neighborhood of a test sample is constructed over the classification knowledge modelled by regional SVMs, and a set of such SVMs adjacent to the test sample is systematically aggregated into a t-SVMT. Compared to a regular SVM and other SVMTs, a t-SVMT, by virtue of the aggregation of SVMs, has an inherent superiority in classifying class-imbalanced datasets. The t-SVMT has also solved the over-fitting problem of all previous SVMTs since it aggregates neighborhood knowledge and thus significantly reduces the size of the SVM tree. The properties of the t-SVMT are evaluated through experiments on a synthetic dataset, eight bench-mark cancer diagnosis datasets, as well as a case study of face membership authentication.