‘Right to be forgotten’: analyzing the impact of forgetting data using K-NN algorithm in data stream learning (2022)
- Authors:
- Autor USP: MASTELINI, SAULO MARTIELLO - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-031-15086-9_34
- Subjects: APRENDIZADO COMPUTACIONAL; ANÁLISE DE SÉRIES TEMPORAIS; DIREITOS E GARANTIAS INDIVIDUAIS
- Keywords: Lazy learning; Stream learning; Right to be forgotten; K-NN; Data stream
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Lecture Notes in Computer Science
- ISSN: 0302-9743
- Volume/Número/Paginação/Ano: v. 13391, p. 530-542, 2022
- Conference titles: IFIP WG 8.5 International Conference on Electronic Government - EGOV
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
LIBERA, Caio Della et al. ‘Right to be forgotten’: analyzing the impact of forgetting data using K-NN algorithm in data stream learning. Lecture Notes in Computer Science. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-15086-9_34. Acesso em: 11 fev. 2026. , 2022 -
APA
Libera, C. D., Miranda, L., Bernardini, F. C., Mastelini, S. M., & Viterbo, J. (2022). ‘Right to be forgotten’: analyzing the impact of forgetting data using K-NN algorithm in data stream learning. Lecture Notes in Computer Science. Cham: Springer. doi:10.1007/978-3-031-15086-9_34 -
NLM
Libera CD, Miranda L, Bernardini FC, Mastelini SM, Viterbo J. ‘Right to be forgotten’: analyzing the impact of forgetting data using K-NN algorithm in data stream learning [Internet]. Lecture Notes in Computer Science. 2022 ; 13391 530-542.[citado 2026 fev. 11 ] Available from: https://doi.org/10.1007/978-3-031-15086-9_34 -
Vancouver
Libera CD, Miranda L, Bernardini FC, Mastelini SM, Viterbo J. ‘Right to be forgotten’: analyzing the impact of forgetting data using K-NN algorithm in data stream learning [Internet]. Lecture Notes in Computer Science. 2022 ; 13391 530-542.[citado 2026 fev. 11 ] Available from: https://doi.org/10.1007/978-3-031-15086-9_34 - Efficient online tree, rule-based and distance-based algorithms
- Towards meta-learning for multi-target regression problems
- River: machine learning for streaming data in Python
- From fault detection to anomaly explanation: a case study on predictive maintenance
- Online anomaly explanation: a case study on predictive maintenance
- DSTARS: a multi-target deep structure for tracking asynchronous regressor
- On ensemble techniques for data stream regression
- Improved prediction of soil properties with multi-target stacked generalisation on EDXRF spectra
- A meta-learning approach for selecting image segmentation algorithm
- Online local boosting: improving performance in online decision trees
Informações sobre o DOI: 10.1007/978-3-031-15086-9_34 (Fonte: oaDOI API)
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
 |
3239195.pdf |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
