An optimal sphere and two large margins approach for novelty detection

Trung Le; Dat Tran; Wanli Ma; Dharmendra Sharma

doi:10.1109/IJCNN.2010.5596456

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 proceeding › Conference Paper › Research › peer-review

22 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 language	English
Title of host publication	The 2010 International Joint Conference on Neural Networks (IJCNN)
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Number of pages	6
ISBN (Print)	9781424469178
DOIs	https://doi.org/10.1109/IJCNN.2010.5596456
Publication status	Published - 2010
Externally published	Yes
Event	IEEE International Joint Conference on Neural Networks 2010 - Barcelona, Spain Duration: 18 Jul 2010 → 23 Jul 2010 https://ieeexplore.ieee.org/xpl/conhome/5581822/proceeding (Proceedings)

Conference

Conference	IEEE International Joint Conference on Neural Networks 2010
Abbreviated title	IJCNN 2010
Country/Territory	Spain
City	Barcelona
Period	18/07/10 → 23/07/10
Internet address	https://ieeexplore.ieee.org/xpl/conhome/5581822/proceeding (Proceedings)

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10.1109/IJCNN.2010.5596456

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title = "An optimal sphere and two large margins approach for novelty detection",

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.",

author = "Trung Le and Dat Tran and Wanli Ma and Dharmendra Sharma",

year = "2010",

doi = "10.1109/IJCNN.2010.5596456",

language = "English",

isbn = "9781424469178",

booktitle = "The 2010 International Joint Conference on Neural Networks (IJCNN)",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "IEEE International Joint Conference on Neural Networks 2010, IJCNN 2010 ; Conference date: 18-07-2010 Through 23-07-2010",

url = "https://ieeexplore.ieee.org/xpl/conhome/5581822/proceeding",

}

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, IEEE International Joint Conference on Neural Networks 2010, Barcelona, Spain, 18/07/10. https://doi.org/10.1109/IJCNN.2010.5596456

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AU - Tran, Dat

AU - Ma, Wanli

AU - Sharma, Dharmendra

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

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DO - 10.1109/IJCNN.2010.5596456

M3 - Conference Paper

AN - SCOPUS:79959415640

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BT - The 2010 International Joint Conference on Neural Networks (IJCNN)

PB - IEEE, Institute of Electrical and Electronics Engineers

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Y2 - 18 July 2010 through 23 July 2010

ER -

An optimal sphere and two large margins approach for novelty detection

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