Spanning SVM Tree for Personalized Transductive Learning

Authors:
Shaoning Pang;Tao Ban;Youki Kadobayashi;Nik Kasabov
Affiliations:
Knowledge Engineering & Discover Research Institute, Auckland University of Technology, Private Bag 92006, Auckland, New Zealand 1020;Information Security Research Center, National Institute of Information and Communications Technology, Tokyo, Japan 184-8795;Information Security Research Center, National Institute of Information and Communications Technology, Tokyo, Japan 184-8795;Knowledge Engineering & Discover Research Institute, Auckland University of Technology, Private Bag 92006, Auckland, New Zealand 1020
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Year:
2009

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Abstract

Personalized Transductive Learning (PTL) builds a unique local model for classification of each test sample and therefore is practically neighborhood dependant. 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, the proposed 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.