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  • Artigo de periodico

    ARM-stream: active recovery of miscategorizations in clustering-based data stream classifiers (2025)

    Cavalcanti, Douglas Monteiro; Cerri, Ricardo ; Faria, Elaine Ribeiro

    Fonte: Data Mining and Knowledge Discovery. Unidade: ICMC

    Assuntos: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS, FRAMEWORKS

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    • ABNT

      CAVALCANTI, Douglas Monteiro e CERRI, Ricardo e FARIA, Elaine Ribeiro. ARM-stream: active recovery of miscategorizations in clustering-based data stream classifiers. Data Mining and Knowledge Discovery, v. 39, n. 5, p. 1-35, 2025Tradução . . Disponível em: https://doi.org/10.1007/s10618-025-01124-4. Acesso em: 12 nov. 2025.

    • APA

      Cavalcanti, D. M., Cerri, R., & Faria, E. R. (2025). ARM-stream: active recovery of miscategorizations in clustering-based data stream classifiers. Data Mining and Knowledge Discovery, 39( 5), 1-35. doi:10.1007/s10618-025-01124-4

    • NLM

      Cavalcanti DM, Cerri R, Faria ER. ARM-stream: active recovery of miscategorizations in clustering-based data stream classifiers [Internet]. Data Mining and Knowledge Discovery. 2025 ; 39( 5): 1-35.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-025-01124-4

    • Vancouver

      Cavalcanti DM, Cerri R, Faria ER. ARM-stream: active recovery of miscategorizations in clustering-based data stream classifiers [Internet]. Data Mining and Knowledge Discovery. 2025 ; 39( 5): 1-35.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-025-01124-4

  • Artigo de periodico

    Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm (2024)

    Raimundo, Marcos M ; Nonato, Luis Gustavo ; Poco, Jorge

    Fonte: Data Mining and Knowledge Discovery. Unidade: ICMC

    Assuntos: APRENDIZADO COMPUTACIONAL, ALGORITMOS

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      RAIMUNDO, Marcos M e NONATO, Luis Gustavo e POCO, Jorge. Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm. Data Mining and Knowledge Discovery, v. 38, p. 2942-2974, 2024Tradução . . Disponível em: https://doi.org/10.1007/s10618-022-00906-4. Acesso em: 12 nov. 2025.

    • APA

      Raimundo, M. M., Nonato, L. G., & Poco, J. (2024). Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm. Data Mining and Knowledge Discovery, 38, 2942-2974. doi:10.1007/s10618-022-00906-4

    • NLM

      Raimundo MM, Nonato LG, Poco J. Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38 2942-2974.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-022-00906-4

    • Vancouver

      Raimundo MM, Nonato LG, Poco J. Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38 2942-2974.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-022-00906-4

  • 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

    Fonte: Data Mining and Knowledge Discovery. Unidade: ICMC

    Assuntos: APRENDIZADO COMPUTACIONAL, ALGORITMOS ÚTEIS E ESPECÍFICOS

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      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: 12 nov. 2025.

    • 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 2025 nov. 12 ] 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 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-024-01002-5

  • Artigo de periodico

    Unsupervised feature based algorithms for time series extrinsic regression (2024)

    Guijo-Rubio, David ; Middlehurst, Matthew ; Arcencio, Guilherme ; Silva, Diego Furtado ; Bagnall, Anthony

    Fonte: Data Mining and Knowledge Discovery. Unidade: ICMC

    Assuntos: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS, ALGORITMOS ÚTEIS E ESPECÍFICOS

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    • ABNT

      GUIJO-RUBIO, David et al. Unsupervised feature based algorithms for time series extrinsic regression. Data Mining and Knowledge Discovery, v. 38, n. 4, p. 2141-2185, 2024Tradução . . Disponível em: https://doi.org/10.1007/s10618-024-01027-w. Acesso em: 12 nov. 2025.

    • APA

      Guijo-Rubio, D., Middlehurst, M., Arcencio, G., Silva, D. F., & Bagnall, A. (2024). Unsupervised feature based algorithms for time series extrinsic regression. Data Mining and Knowledge Discovery, 38( 4), 2141-2185. doi:10.1007/s10618-024-01027-w

    • NLM

      Guijo-Rubio D, Middlehurst M, Arcencio G, Silva DF, Bagnall A. Unsupervised feature based algorithms for time series extrinsic regression [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38( 4): 2141-2185.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-024-01027-w

    • Vancouver

      Guijo-Rubio D, Middlehurst M, Arcencio G, Silva DF, Bagnall A. Unsupervised feature based algorithms for time series extrinsic regression [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38( 4): 2141-2185.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-024-01027-w

  • Artigo de periodico

    MINAS: multiclass learning algorithm for novelty detection in data streams (2016)

    Faria, Elaine Ribeiro de; Carvalho, André Carlos Ponce de Leon Ferreira de ; Gama, João

    Fonte: Data Mining and Knowledge Discovery. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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    • ABNT

      FARIA, Elaine Ribeiro de e CARVALHO, André Carlos Ponce de Leon Ferreira de e GAMA, João. MINAS: multiclass learning algorithm for novelty detection in data streams. Data Mining and Knowledge Discovery, v. 30, n. 3, p. 640-680, 2016Tradução . . Disponível em: https://doi.org/10.1007/s10618-015-0433-y. Acesso em: 12 nov. 2025.

    • APA

      Faria, E. R. de, Carvalho, A. C. P. de L. F. de, & Gama, J. (2016). MINAS: multiclass learning algorithm for novelty detection in data streams. Data Mining and Knowledge Discovery, 30( 3), 640-680. doi:10.1007/s10618-015-0433-y

    • NLM

      Faria ER de, Carvalho ACP de LF de, Gama J. MINAS: multiclass learning algorithm for novelty detection in data streams [Internet]. Data Mining and Knowledge Discovery. 2016 ; 30( 3): 640-680.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-015-0433-y

    • Vancouver

      Faria ER de, Carvalho ACP de LF de, Gama J. MINAS: multiclass learning algorithm for novelty detection in data streams [Internet]. Data Mining and Knowledge Discovery. 2016 ; 30( 3): 640-680.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-015-0433-y

  • Artigo de periodico

    Ensembles of label noise filters: a ranking approach (2016)

    Garcia, Luís P. F; Lorena, Ana C; Matwin, Stan; Carvalho, André Carlos Ponce de Leon Ferreira de

    Fonte: Data Mining and Knowledge Discovery. Unidade: ICMC

    Assuntos: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL, MINERAÇÃO DE DADOS

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    • ABNT

      GARCIA, Luís P. F et al. Ensembles of label noise filters: a ranking approach. Data Mining and Knowledge Discovery, v. 30, n. 5, p. 1192-1216, 2016Tradução . . Disponível em: https://doi.org/10.1007/s10618-016-0475-9. Acesso em: 12 nov. 2025.

    • APA

      Garcia, L. P. F., Lorena, A. C., Matwin, S., & Carvalho, A. C. P. de L. F. de. (2016). Ensembles of label noise filters: a ranking approach. Data Mining and Knowledge Discovery, 30( 5), 1192-1216. doi:10.1007/s10618-016-0475-9

    • NLM

      Garcia LPF, Lorena AC, Matwin S, Carvalho ACP de LF de. Ensembles of label noise filters: a ranking approach [Internet]. Data Mining and Knowledge Discovery. 2016 ; 30( 5): 1192-1216.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-016-0475-9

    • Vancouver

      Garcia LPF, Lorena AC, Matwin S, Carvalho ACP de LF de. Ensembles of label noise filters: a ranking approach [Internet]. Data Mining and Knowledge Discovery. 2016 ; 30( 5): 1192-1216.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-016-0475-9
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