Personalized mode transductive spanning SVM classification tree

Authors:
Shaoning Pang;Tao Ban;Youki Kadobayashi;Nikola Kasabov
Affiliations:
Department of Computing, Unitec Institute of Technology, Private Bag 92025, New Zealand;Information Security Research Center, National Institute of Information and Communications Technology, 184-8795, Tokyo, Japan;Graduate School of Information Science, Nara Institute of Science and Technology, Japan;Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1020, New Zealand
Venue:
Information Sciences: an International Journal
Year:
2011

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Abstract

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.