An empirical study on anomaly detection algorithms for extremely imbalanced datasets

Anomaly detection attempts to identify abnormal events that deviate from normality. Since such events are often rare, data related to this domain is usually imbalanced. In this paper, we compare diverse preprocessing and Machine Learning (ML) state-of-the-art algorithms that can be adopted within th...

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Bibliographic Details
Main Author: Fontes, Gonçalo (author)
Other Authors: Matos, Luís Miguel (author), Matta, Arthur (author), Pilastri, André Luiz (author), Cortez, Paulo (author)
Format: conferencePaper
Language:eng
Published: 2022
Subjects:
Autoencoder
AutoML
Deep learning
Isolation Forest
One-class classification
Random Forest
Supervised learning
Unsupervised learning
Ciências Naturais::Ciências da Computação e da Informação
Online Access:https://hdl.handle.net/1822/81441
Country:Portugal
Oai:oai:repositorium.sdum.uminho.pt:1822/81441

Internet

https://hdl.handle.net/1822/81441

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