Author: Qiao Xiaoshuang
Publisher: Edp Sciences
E-ISSN: 2261-236x|189|issue|03008-03008
ISSN: 2261-236x
Source: MATEC Web of conference, Vol.189, Iss.issue, 2018-08, pp. : 03008-03008
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Abstract
This paper explores a new ensemble approach called Ensemble Probability Distribution Novelty Detection (EPDND) for novelty detection. The proposed ensemble approach provides a metric to characterize different classes. Experimental results on 4 real-world datasets show that EPDND exhibits competitive overall performance to the other two common novelty detection approaches - Support Vector Domain Description and Gaussian Mixed Models in terms of accuracy, recall and F1 scores in many cases.
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