Spanning SVM Tree for Personalized Transductive Learning

  • Shaoning Pang
  • Tao Ban
  • Youki Kadobayashi
  • Nik Kasabov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5768)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Shaoning Pang
    • 1
  • Tao Ban
    • 2
  • Youki Kadobayashi
    • 2
  • Nik Kasabov
    • 1
  1. 1.Knowledge Engineering & Discover Research InstituteAuckland University of Technology, Private Bag 92006AucklandNew Zealand
  2. 2.Information Security Research CenterNational Institute of Information and Communications TechnologyTokyoJapan

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