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

16 Citations (Scopus)

Abstract

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
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona, Spain
Duration: 18 Jul 201023 Jul 2010

Conference

Conference2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
CountrySpain
CityBarcelona
Period18/07/1023/07/10

Cite this

Le, T., Tran, D., Ma, W., & Sharma, D. (2010). An optimal sphere and two large margins approach for novelty detection. In The 2010 International Joint Conference on Neural Networks (IJCNN) [5596456] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2010.5596456