Ensemble probability distribution for novelty detection
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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.
2017 ◽
Vol 27
(1)
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pp. 169-180
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2022 ◽
Vol 17
(1)
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pp. 1-17
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2016 ◽
Vol 24
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pp. 219-233
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2019 ◽
Vol 33
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pp. 5516-5524