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...
| Autor principal: | |
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| Outros Autores: | , , , |
| Formato: | conferencePaper |
| Idioma: | eng |
| Publicado em: |
2022
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| Assuntos: | |
| Texto completo: | https://hdl.handle.net/1822/81441 |
| País: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/81441 |