Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/87713
Título: | AutoOC: A Python module for automated multi-objective One-Class Classification |
Autor(es): | Ferreira, Luís Cortez, Paulo |
Palavras-chave: | Automated machine learning Deep autoencoders Grammatical Evolution Multi-objective optimization One-Class Classification Python |
Data: | 2023 |
Editora: | Elsevier 1 |
Revista: | Software Impacts |
Citação: | Ferreira, L., & Cortez, P. (2023, November). AutoOC: A Python module for automated multi-objective One-Class Classification. Software Impacts. Elsevier BV. http://doi.org/10.1016/j.simpa.2023.100590 |
Resumo(s): | AutoOC is an open-source Python module to efficiently automate the selection and hyperparameter tuning of quality OCC (One-Class Classification) learners. By using a GE (Grammatical Evolution) approach, AutoOC searches for five base learners, namely IF (Isolation Forest), LOF (Local Outlier Factor), OC-SVM (One-Class SVM), AE (Autoencoder), and VAE (Variational Autoencoder). The module provides a multi-objective search, where predictive performance and computational efficiency are simultaneously optimized. By providing a simple set of functions, AutoOC allows the user to easily generate OCC models for a dataset, being well-suited for anomaly detection tasks, where most of the data is composed of normal records. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/87713 |
DOI: | 10.1016/j.simpa.2023.100590 |
Versão da editora: | https://www.softwareimpacts.com/article/S2665-9638(23)00127-6/fulltext |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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simpac.pdf | 624,97 kB | Adobe PDF | Ver/Abrir |