On the evaluation of outlier detection and one-class classification methods (2016)
- Authors:
- Autor USP: CAMPELLO, RICARDO JOSÉ GABRIELLI BARRETO - ICMC
- Unidade: ICMC
- DOI: 10.1109/DSAA.2016.8
- Subjects: INTELIGÊNCIA ARTIFICIAL; APRENDIZADO COMPUTACIONAL; RECONHECIMENTO DE PADRÕES
- Keywords: semi-supervised learning; unsupervised learning; one-class classification; outlier detection; machine learning algorithms; predictive models; evaluation
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Los Alamitos
- Date published: 2016
- Source:
- Título: Proceedings
- Conference titles: IEEE International Conference on Data Science and Advanced Analytics - DSAA
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
SWERSKY, Lorne et al. On the evaluation of outlier detection and one-class classification methods. 2016, Anais.. Los Alamitos: IEEE, 2016. Disponível em: https://doi.org/10.1109/DSAA.2016.8. Acesso em: 12 maio 2025. -
APA
Swersky, L., Marques, H. O., Sander, J., Campello, R. J. G. B., & Zimek, A. (2016). On the evaluation of outlier detection and one-class classification methods. In Proceedings. Los Alamitos: IEEE. doi:10.1109/DSAA.2016.8 -
NLM
Swersky L, Marques HO, Sander J, Campello RJGB, Zimek A. On the evaluation of outlier detection and one-class classification methods [Internet]. Proceedings. 2016 ;[citado 2025 maio 12 ] Available from: https://doi.org/10.1109/DSAA.2016.8 -
Vancouver
Swersky L, Marques HO, Sander J, Campello RJGB, Zimek A. On the evaluation of outlier detection and one-class classification methods [Internet]. Proceedings. 2016 ;[citado 2025 maio 12 ] Available from: https://doi.org/10.1109/DSAA.2016.8 - Texto sistematizado
- Modelagem e controle utilizando bases de funções ortonormais.
- Automatic aspect discrimination in relational data clustering
- An introduction to models based on Laguerre, Kautz and other related orthonormal functions - part II: non-linear models
- Evaluating correlation coefficients for clustering gene expression profiles of cancer
- A simpler and more accurate AUTO-HDS framework for clustering and visualization of biological data
- On the combination of relative clustering validity criteria
- Evolutionary k-means for distributed data sets
- Active learning strategies for semi-supervised DBSCAN
- Asymmetric Volterra models based on ladder-structured generalized orthonormal basis functions
Informações sobre o DOI: 10.1109/DSAA.2016.8 (Fonte: oaDOI API)
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
