Guest editorial : Thematic issue on ‘ Adaptive Soft Computing

Personalized Transductive Learning (PTL) builds a unique local model for classification of each test sample and therefore is 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, in this paper we introduce a new concept of knowledgeable neighborhood and a transductive 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 are aggregated systematically into a t-SVMT. Compared to a regular SVM and other SVMTs, t-SVMT, by virtue of the aggregation of SVMs, has an inherent superiority on classifying class-imbalanced datasets. Furthermore, t-SVMT has solved the over-fitting problem of all previous SVMTs as it aggregates neighborhood knowledge and thus significantly reduces the size of the SVM tree. The properties of t-SVMT are evaluated through experiments on a synthetic dataset, 8 bench-mark cancer diagnosis datasets, as well as a case study of face membership authentication.