MATEC Web Conf.
Volume 189, 20182018 2nd International Conference on Material Engineering and Advanced Manufacturing Technology (MEAMT 2018)
|Number of page(s)||8|
|Section||Cloud & Network|
|Published online||10 August 2018|
Ensemble probability distribution for novelty detection
Digit Fujian Internet-of-Things Laboratory of Environmental Monitoring, School of Mathematics and Information, Fujian Normal University, Fuzhou, P.R.China
2 School of Computing and Mathematics University of Ulster at Jordanstown, Northern Ireland, UK
* Corresponding author: email@example.com
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.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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