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Artigo de periodico
Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms (2024)
Mantovani, Rafael Gomes ; Horváth, Tomás ; Rossi, André Luis Debiaso ; Cerri, Ricardo ; Barbon Júnior, Sylvio ; Vanschoren, Joaquin ; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ALGORITMOS ÚTEIS E ESPECÍFICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MANTOVANI, Rafael Gomes et al. Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms. Data Mining and Knowledge Discovery, v. 38, n. 3, p. 1364-1416, 2024Tradução . . Disponível em: https://doi.org/10.1007/s10618-024-01002-5. Acesso em: 14 nov. 2024.
APA
Mantovani, R. G., Horváth, T., Rossi, A. L. D., Cerri, R., Barbon Júnior, S., Vanschoren, J., & Carvalho, A. C. P. de L. F. de. (2024). Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms. Data Mining and Knowledge Discovery, 38( 3), 1364-1416. doi:10.1007/s10618-024-01002-5
NLM
Mantovani RG, Horváth T, Rossi ALD, Cerri R, Barbon Júnior S, Vanschoren J, Carvalho ACP de LF de. Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38( 3): 1364-1416.[citado 2024 nov. 14 ] Available from: https://doi.org/10.1007/s10618-024-01002-5
Vancouver
Mantovani RG, Horváth T, Rossi ALD, Cerri R, Barbon Júnior S, Vanschoren J, Carvalho ACP de LF de. Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38( 3): 1364-1416.[citado 2024 nov. 14 ] Available from: https://doi.org/10.1007/s10618-024-01002-5
Trabalho de evento
CallMine: fraud detection and visualization of million-scale call graphs (2023)
Cazzolato, Mirela Teixeira ; Vijayakumar, Saranya ; Lee, Meng-Chieh; Vajiac, Catalina; Park, Namyong ; Fidalgo, Pedro; Traina, Agma Juci Machado ; Faloutsos, Christos
Source: Proceedings. Conference titles: ACM International Conference on Information and Knowledge Management - CIKM. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, MINERAÇÃO DE DADOS, VISUALIZAÇÃO, FRAUDE BANCÁRIA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CAZZOLATO, Mirela Teixeira et al. CallMine: fraud detection and visualization of million-scale call graphs. 2023, Anais.. New York: ACM, 2023. Disponível em: https://doi.org/10.1145/3583780.3614662. Acesso em: 14 nov. 2024.
APA
Cazzolato, M. T., Vijayakumar, S., Lee, M. -C., Vajiac, C., Park, N., Fidalgo, P., et al. (2023). CallMine: fraud detection and visualization of million-scale call graphs. In Proceedings. New York: ACM. doi:10.1145/3583780.3614662
NLM
Cazzolato MT, Vijayakumar S, Lee M-C, Vajiac C, Park N, Fidalgo P, Traina AJM, Faloutsos C. CallMine: fraud detection and visualization of million-scale call graphs [Internet]. Proceedings. 2023 ;[citado 2024 nov. 14 ] Available from: https://doi.org/10.1145/3583780.3614662
Vancouver
Cazzolato MT, Vijayakumar S, Lee M-C, Vajiac C, Park N, Fidalgo P, Traina AJM, Faloutsos C. CallMine: fraud detection and visualization of million-scale call graphs [Internet]. Proceedings. 2023 ;[citado 2024 nov. 14 ] Available from: https://doi.org/10.1145/3583780.3614662
Trabalho de evento
Exploratory data analysis in electronic health records graphs: intuitive features and visualization tools (2023)
Cazzolato, Mirela Teixeira ; Gutierrez, Marco Antonio ; Traina Junior, Caetano ; Faloutsos, Christos ; Traina, Agma Juci Machado
Source: Proceedings. Conference titles: International Symposium on Computer-Based Medical Systems - CBMS. Unidade: ICMC
Subjects: BANCO DE DADOS, RECUPERAÇÃO DA INFORMAÇÃO, VISUALIZAÇÃO, REGISTROS HOSPITALARES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CAZZOLATO, Mirela Teixeira et al. Exploratory data analysis in electronic health records graphs: intuitive features and visualization tools. 2023, Anais.. Los Alamitos: IEEE, 2023. Disponível em: https://doi.org/10.1109/CBMS58004.2023.00202. Acesso em: 14 nov. 2024.
APA
Cazzolato, M. T., Gutierrez, M. A., Traina Junior, C., Faloutsos, C., & Traina, A. J. M. (2023). Exploratory data analysis in electronic health records graphs: intuitive features and visualization tools. In Proceedings. Los Alamitos: IEEE. doi:10.1109/CBMS58004.2023.00202
NLM
Cazzolato MT, Gutierrez MA, Traina Junior C, Faloutsos C, Traina AJM. Exploratory data analysis in electronic health records graphs: intuitive features and visualization tools [Internet]. Proceedings. 2023 ;[citado 2024 nov. 14 ] Available from: https://doi.org/10.1109/CBMS58004.2023.00202
Vancouver
Cazzolato MT, Gutierrez MA, Traina Junior C, Faloutsos C, Traina AJM. Exploratory data analysis in electronic health records graphs: intuitive features and visualization tools [Internet]. Proceedings. 2023 ;[citado 2024 nov. 14 ] Available from: https://doi.org/10.1109/CBMS58004.2023.00202
Trabalho de evento
TGRAPP: anomaly detection and visualization of large-scale call graphs (2023)
Cazzolato, Mirela Teixeira ; Vijayakumar, Saranya; Zheng, Xinyi; Park, Namyong; Lee, Meng-Chieh; Chau, Duen Horng ; Fidalgo, Pedro; Lages, Bruno; Traina, Agma Juci Machado ; Faloutsos, Christos
Source: Proceedings. Conference titles: AAAI Conference on Artificial Intelligence - AAAI. Unidade: ICMC
Subjects: RECUPERAÇÃO DA INFORMAÇÃO, VISUALIZAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CAZZOLATO, Mirela Teixeira et al. TGRAPP: anomaly detection and visualization of large-scale call graphs. 2023, Anais.. Washington: AAAI Press, 2023. Disponível em: https://doi.org/10.1609/aaai.v37i13.27062. Acesso em: 14 nov. 2024.
APA
Cazzolato, M. T., Vijayakumar, S., Zheng, X., Park, N., Lee, M. -C., Chau, D. H., et al. (2023). TGRAPP: anomaly detection and visualization of large-scale call graphs. In Proceedings. Washington: AAAI Press. doi:10.1609/aaai.v37i13.27062
NLM
Cazzolato MT, Vijayakumar S, Zheng X, Park N, Lee M-C, Chau DH, Fidalgo P, Lages B, Traina AJM, Faloutsos C. TGRAPP: anomaly detection and visualization of large-scale call graphs [Internet]. Proceedings. 2023 ;[citado 2024 nov. 14 ] Available from: https://doi.org/10.1609/aaai.v37i13.27062
Vancouver
Cazzolato MT, Vijayakumar S, Zheng X, Park N, Lee M-C, Chau DH, Fidalgo P, Lages B, Traina AJM, Faloutsos C. TGRAPP: anomaly detection and visualization of large-scale call graphs [Internet]. Proceedings. 2023 ;[citado 2024 nov. 14 ] Available from: https://doi.org/10.1609/aaai.v37i13.27062
Artigo de periodico
Extended recommendation-by-explanation (2022)
Rana, Arplt ; D'Addio, Rafael Martins ; Manzato, Marcelo Garcia ; Bridge, Derek
Source: User Modeling and User-Adapted Interaction. Unidade: ICMC
Subjects: RECONHECIMENTO DE PADRÕES, RECUPERAÇÃO DA INFORMAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RANA, Arplt et al. Extended recommendation-by-explanation. User Modeling and User-Adapted Interaction, v. 32, n. 1-2, p. 91-131, 2022Tradução . . Disponível em: https://doi.org/10.1007/s11257-021-09317-4. Acesso em: 14 nov. 2024.
APA
Rana, A., D'Addio, R. M., Manzato, M. G., & Bridge, D. (2022). Extended recommendation-by-explanation. User Modeling and User-Adapted Interaction, 32( 1-2), 91-131. doi:10.1007/s11257-021-09317-4
NLM
Rana A, D'Addio RM, Manzato MG, Bridge D. Extended recommendation-by-explanation [Internet]. User Modeling and User-Adapted Interaction. 2022 ; 32( 1-2): 91-131.[citado 2024 nov. 14 ] Available from: https://doi.org/10.1007/s11257-021-09317-4
Vancouver
Rana A, D'Addio RM, Manzato MG, Bridge D. Extended recommendation-by-explanation [Internet]. User Modeling and User-Adapted Interaction. 2022 ; 32( 1-2): 91-131.[citado 2024 nov. 14 ] Available from: https://doi.org/10.1007/s11257-021-09317-4
Trabalho de evento
TgraphSpot: fast and effective anomaly detection for time-evolving graphs (2022)
Cazzolato, Mirela Teixeira ; Vijayakumar, Saranya; Zheng, Xinyi; Park, Namyong; Lee, Meng-Chieh; Fidalgo, Pedro; Lages, Bruno; Traina, Agma Juci Machado ; Faloutsos, Christos
Source: Proceedings. Conference titles: IEEE International Conference on Big Data - Big Data. Unidade: ICMC
Subjects: RECUPERAÇÃO DA INFORMAÇÃO, VISUALIZAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CAZZOLATO, Mirela Teixeira et al. TgraphSpot: fast and effective anomaly detection for time-evolving graphs. 2022, Anais.. Piscataway: IEEE, 2022. Disponível em: https://doi.org/10.1109/BigData55660.2022.10020898. Acesso em: 14 nov. 2024.
APA
Cazzolato, M. T., Vijayakumar, S., Zheng, X., Park, N., Lee, M. -C., Fidalgo, P., et al. (2022). TgraphSpot: fast and effective anomaly detection for time-evolving graphs. In Proceedings. Piscataway: IEEE. doi:10.1109/BigData55660.2022.10020898
NLM
Cazzolato MT, Vijayakumar S, Zheng X, Park N, Lee M-C, Fidalgo P, Lages B, Traina AJM, Faloutsos C. TgraphSpot: fast and effective anomaly detection for time-evolving graphs [Internet]. Proceedings. 2022 ;[citado 2024 nov. 14 ] Available from: https://doi.org/10.1109/BigData55660.2022.10020898
Vancouver
Cazzolato MT, Vijayakumar S, Zheng X, Park N, Lee M-C, Fidalgo P, Lages B, Traina AJM, Faloutsos C. TgraphSpot: fast and effective anomaly detection for time-evolving graphs [Internet]. Proceedings. 2022 ;[citado 2024 nov. 14 ] Available from: https://doi.org/10.1109/BigData55660.2022.10020898

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