An optimal sphere and two large margins approach for novelty detection

Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

22 Citations (Scopus)


We introduce a new model to deal with imbalanced data sets for novelty detection problems where the normal class of training data set can be majority or minority class. The key idea is to construct an optimal hypersphere such that the inside margin between the surface of this sphere and the normal data and the outside margin between that surface and the abnormal data are as large as possible. Depending on a specific real application of novelty detection, the two margins can be adjusted to achieve the best true positive and false positive rates. Experimental results on a number of data sets showed that the proposed model can provide better performance comparing with current models for novelty detection.

Original languageEnglish
Title of host publicationThe 2010 International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Print)9781424469178
Publication statusPublished - 2010
Externally publishedYes
EventIEEE International Joint Conference on Neural Networks 2010 - Barcelona, Spain
Duration: 18 Jul 201023 Jul 2010 (Proceedings)


ConferenceIEEE International Joint Conference on Neural Networks 2010
Abbreviated titleIJCNN 2010
Internet address

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