Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33369
Title: Structured one-class classification
Authors: Wang, D
Yeung, DS
Tsang, ECC
Keywords: One-class classification
Second-order cone programming (SOCP)
Structured learning
Support vector machine (SVM)
Issue Date: 2006
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, 2006, v. 36, no. 6, p. 1283-1294 How to cite?
Journal: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics 
Abstract: The one-class classification problem aims to distinguish a target class from outliers. The spherical one-class classifier (SOCC) solves this problem by finding a hypersphere with minimum volume that contains the target data while keeping outlier samples outside. SOCC achieves satisfactory performance only when the target samples have the same distribution tendency in all orientations. Therefore, the performance of the SOCC is limited in the way that many superfluous outliers might be mistakenly enclosed. The authors propose to exploit target data structures obtained via unsupervised methods such as agglomerative hierarchical clustering and use them in calculating a set of hyperellipsoidal separating boundaries. This method is named the structured one-class classifier (TOCC). The optimization problem in TOCC can be formulated as a series of second-order cone programming problems that can be solved with acceptable efficiency by primal-dual interior-point methods. The experimental results on artificially generated data sets and benchmark data sets demonstrate the advantages of TOCC.
URI: http://hdl.handle.net/10397/33369
ISSN: 1083-4419
DOI: 10.1109/TSMCB.2006.876189
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