Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/81441
Título: | An empirical study on anomaly detection algorithms for extremely imbalanced datasets |
Autor(es): | Fontes, Gonçalo Matos, Luís Miguel Matta, Arthur Pilastri, André Luiz Cortez, Paulo |
Palavras-chave: | Autoencoder AutoML Deep learning Isolation Forest One-class classification Random Forest Supervised learning Unsupervised learning |
Data: | 1-Jan-2022 |
Editora: | Springer, Cham |
Revista: | IFIP Advances in Information and Communication Technology |
Citação: | Fontes, G., Matos, L.M., Matta, A., Pilastri, A., Cortez, P. (2022). An Empirical Study on Anomaly Detection Algorithms for Extremely Imbalanced Datasets. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 646. Springer, Cham. https://doi.org/10.1007/978-3-031-08333-4_7 |
Resumo(s): | 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 this anomaly detection context. These include two unsupervised learning algorithms, namely Isolation Forests (IF) and deep dense AutoEncoders (AE), and two supervised learning approaches, namely Random Forest and an Automated ML (AutoML) method. Several empirical experiments were conducted by adopting seven extremely imbalanced public domain datasets. Overall, the IF and AE unsupervised methods obtained competitive anomaly detection results, which also have the advantage of not requiring labeled data. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/81441 |
ISBN: | 978-3-031-08332-7 |
e-ISBN: | 978-3-031-08333-4 |
DOI: | 10.1007/978-3-031-08333-4_7 |
ISSN: | 1868-4238 |
Versão da editora: | https://link.springer.com/chapter/10.1007/978-3-031-08333-4_7 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
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Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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AIAI2022_paper_90.pdf | 173,37 kB | Adobe PDF | Ver/Abrir |