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pro vyhledávání: '"Firdova, Katarina"'
This paper presents a new filter method for unsupervised feature selection. This method is particularly effective on imbalanced multi-class dataset, as in case of clusters of different anomaly types. Existing methods usually involve the variance of t
Externí odkaz:
http://arxiv.org/abs/2305.19804
In this paper we describe an approach for anomaly detection and its explainability in multivariate functional data. The anomaly detection procedure consists of transforming the series into a vector of features and using an Isolation forest algorithm.
Externí odkaz:
http://arxiv.org/abs/2205.02935