Anomaly Detection With Multivariate K-sigma Score Using Monte Carlo
Küçük Resim Yok
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
We propose a new unsupervised anomaly detection technique based on Monte Carlo sampling. We draw samples from a Gaussian probability distribution that fits to the given data and use these samples to form a Monte Carlo estimate of an anomaly score. Many of the current anomaly detection algorithms detect anomalies without producing any anomaly scores. This may lead to confusion over the scaling of the anomaly situation as well as identifying the underlying cause for anomalies in explainable conditions. The proposed algorithm is a multi-variate unsupervised technique, which also provides anomaly scores that allows measuring the strength of outlierness of a multi-variate data point. Our experiments show that the proposed algorithm achieves better results compared to the state-of-the-art anomaly detection algorithms with clustering-based approaches and isolation forest.
Açıklama
5th International Conference on Computer Science and Engineering (UBMK) -- SEP 09-11, 2020 -- Diyarbakir, TURKEY
Anahtar Kelimeler
Anomaly Detection, Clustering, Monte Carlo Sampling, Isolation Forest, Machine Learning
Kaynak
2020 5th International Conference on Computer Science and Engineering (Ubmk)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A