Accession Number : AD1015855

Title :   Repulsive-SVDD Classification

Descriptive Note : Conference Paper

Corporate Author : University of Canberra Canberra Australia

Personal Author(s) : Nguyen,Phuoc ; Tran,Dat

Full Text :

Report Date : 22 May 2015

Pagination or Media Count : 12

Abstract : Support vector data description (SVDD) is a well-known kernel method that constructs a minimal hypersphere regarded as a data description for a given data set. However SVDD does not take into account any statistical distribution of the data set in constructing that optimal hypersphere, and SVDD is applied to solving one-class classification problems only. This paper proposes a new approach to SVDD to address those limitations. We formulate an optimisation problem for binary classification in which we construct two hyperspheres, one enclosing positive samples and the other enclosing negative samples, and during the optimisation process we move the two hyperspheres apart to maximize the margin between them while the data samples of each class are still inside their own hyperspheres. Experimental results show good performance for the proposed method.

Descriptors :   data mining , knowledge based systems , classification , supervised machine learning , change detection , kernel functions , lagrangian functions , inequalities , relational data bases , experimental design , information retrieval

Distribution Statement : APPROVED FOR PUBLIC RELEASE