Spanning SVM Tree for Personalized Transductive Learning
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.
KeywordsData Partition Query Sample Transductive Learn Class Label Information Inherent Superiority
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