An optimal sphere and two large margins approach for novelty detection

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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 publication2010 International Joint Conference on Neural Networks (IJCNN)
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781424469185
ISBN (Print)9781424469161
Publication statusPublished - 2010
Event2010 IEEE World Congress on Computational Intelligence (FUZZ-IEEE 2010) - Barcelona, Barcelona, Spain
Duration: 18 Jul 201023 Jul 2010


Conference2010 IEEE World Congress on Computational Intelligence (FUZZ-IEEE 2010)

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