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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
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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: 03 jun. 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 jun. 03 ] 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 jun. 03 ] 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
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Subjects: 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, 2024Tradução . . Disponível em: https://doi.org/10.1007/s10618-024-01027-w. Acesso em: 03 jun. 2024.
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. 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 ;[citado 2024 jun. 03 ] 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 ;[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-024-01027-w
Artigo de periodico
Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm (2022)
Raimundo, Marcos M ; Nonato, Luis Gustavo ; Poco, Jorge
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ALGORITMOS
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ABNT
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, 2022Tradução . . Disponível em: https://doi.org/10.1007/s10618-022-00906-4. Acesso em: 03 jun. 2024.
APA
Raimundo, M. M., Nonato, L. G., & Poco, J. (2022). Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm. Data Mining and Knowledge Discovery. 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. 2022 ;[citado 2024 jun. 03 ] 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. 2022 ;[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-022-00906-4
Artigo de periodico
Challenges in benchmarking stream learning algorithms with real-world data (2020)
Souza, Vinícius Mourão Alves de ; Reis, Denis Moreira dos ; Maletzke, André Gustavo ; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Subjects: ANÁLISE DE SÉRIES TEMPORAIS, MINERAÇÃO DE DADOS, ALGORITMOS ÚTEIS E ESPECÍFICOS, BENCHMARKS
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ABNT
SOUZA, Vinícius Mourão Alves de et al. Challenges in benchmarking stream learning algorithms with real-world data. Data Mining and Knowledge Discovery, v. No 2020, n. 6, p. 1805-1858, 2020Tradução . . Disponível em: https://doi.org/10.1007/s10618-020-00698-5. Acesso em: 03 jun. 2024.
APA
Souza, V. M. A. de, Reis, D. M. dos, Maletzke, A. G., & Batista, G. E. de A. P. A. (2020). Challenges in benchmarking stream learning algorithms with real-world data. Data Mining and Knowledge Discovery, No 2020( 6), 1805-1858. doi:10.1007/s10618-020-00698-5
NLM
Souza VMA de, Reis DM dos, Maletzke AG, Batista GE de APA. Challenges in benchmarking stream learning algorithms with real-world data [Internet]. Data Mining and Knowledge Discovery. 2020 ; No 2020( 6): 1805-1858.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-020-00698-5
Vancouver
Souza VMA de, Reis DM dos, Maletzke AG, Batista GE de APA. Challenges in benchmarking stream learning algorithms with real-world data [Internet]. Data Mining and Knowledge Discovery. 2020 ; No 2020( 6): 1805-1858.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-020-00698-5
Artigo de periodico
A unified view of density-based methods for semi-supervised clustering and classification (2019)
Gertrudes, Jadson Castro ; Zimek, Arthur ; Sander, Jörg ; Campello, Ricardo José Gabrielli Barreto
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
GERTRUDES, Jadson Castro et al. A unified view of density-based methods for semi-supervised clustering and classification. Data Mining and Knowledge Discovery, v. No 2019, n. 6, p. 1894-1952, 2019Tradução . . Disponível em: https://doi.org/10.1007/s10618-019-00651-1. Acesso em: 03 jun. 2024.
APA
Gertrudes, J. C., Zimek, A., Sander, J., & Campello, R. J. G. B. (2019). A unified view of density-based methods for semi-supervised clustering and classification. Data Mining and Knowledge Discovery, No 2019( 6), 1894-1952. doi:10.1007/s10618-019-00651-1
NLM
Gertrudes JC, Zimek A, Sander J, Campello RJGB. A unified view of density-based methods for semi-supervised clustering and classification [Internet]. Data Mining and Knowledge Discovery. 2019 ; No 2019( 6): 1894-1952.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-019-00651-1
Vancouver
Gertrudes JC, Zimek A, Sander J, Campello RJGB. A unified view of density-based methods for semi-supervised clustering and classification [Internet]. Data Mining and Knowledge Discovery. 2019 ; No 2019( 6): 1894-1952.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-019-00651-1
Artigo de periodico
Speeding up similarity search under dynamic time warping by pruning unpromising alignments (2018)
Silva, Diego F; Giusti, Rafael; Keogh, Eamonn; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Subjects: MINERAÇÃO DE DADOS, ANÁLISE DE SÉRIES TEMPORAIS, RECONHECIMENTO DE PADRÕES
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ABNT
SILVA, Diego F et al. Speeding up similarity search under dynamic time warping by pruning unpromising alignments. Data Mining and Knowledge Discovery, v. 32, n. 4, p. 988-1016, 2018Tradução . . Disponível em: https://doi.org/10.1007/s10618-018-0557-y. Acesso em: 03 jun. 2024.
APA
Silva, D. F., Giusti, R., Keogh, E., & Batista, G. E. de A. P. A. (2018). Speeding up similarity search under dynamic time warping by pruning unpromising alignments. Data Mining and Knowledge Discovery, 32( 4), 988-1016. doi:10.1007/s10618-018-0557-y
NLM
Silva DF, Giusti R, Keogh E, Batista GE de APA. Speeding up similarity search under dynamic time warping by pruning unpromising alignments [Internet]. Data Mining and Knowledge Discovery. 2018 ; 32( 4): 988-1016.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-018-0557-y
Vancouver
Silva DF, Giusti R, Keogh E, Batista GE de APA. Speeding up similarity search under dynamic time warping by pruning unpromising alignments [Internet]. Data Mining and Knowledge Discovery. 2018 ; 32( 4): 988-1016.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-018-0557-y
Artigo de periodico
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study (2016)
Campos, Guilherme O; Zimek, Arthur; Sander, Jörg; Campello, Ricardo José Gabrielli Barreto; Micenková, Barbora; Schubert, Erich; Assent, Ira; Houle, Michael E
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
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ABNT
CAMPOS, Guilherme O et al. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery, v. 30, n. 4, p. 891-927, 2016Tradução . . Disponível em: https://doi.org/10.1007/s10618-015-0444-8. Acesso em: 03 jun. 2024.
APA
Campos, G. O., Zimek, A., Sander, J., Campello, R. J. G. B., Micenková, B., Schubert, E., et al. (2016). On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery, 30( 4), 891-927. doi:10.1007/s10618-015-0444-8
NLM
Campos GO, Zimek A, Sander J, Campello RJGB, Micenková B, Schubert E, Assent I, Houle ME. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study [Internet]. Data Mining and Knowledge Discovery. 2016 ; 30( 4): 891-927.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-015-0444-8
Vancouver
Campos GO, Zimek A, Sander J, Campello RJGB, Micenková B, Schubert E, Assent I, Houle ME. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study [Internet]. Data Mining and Knowledge Discovery. 2016 ; 30( 4): 891-927.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-015-0444-8
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
Source: 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: 03 jun. 2024.
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 2024 jun. 03 ] 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 2024 jun. 03 ] 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
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Subjects: 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: 03 jun. 2024.
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 2024 jun. 03 ] 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 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-016-0475-9
Artigo de periodico
CID: an efficient complexity-invariant distance for time series (2014)
Batista, Gustavo Enrique de Almeida Prado Alves; Keogh, Eamonn J; Tataw, Oben Moses; Souza, Vinícius M. A. de
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
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ABNT
BATISTA, Gustavo Enrique de Almeida Prado Alves et al. CID: an efficient complexity-invariant distance for time series. Data Mining and Knowledge Discovery, v. 28, n. 3, p. 634-669, 2014Tradução . . Disponível em: https://doi.org/10.1007/s10618-013-0312-3. Acesso em: 03 jun. 2024.
APA
Batista, G. E. de A. P. A., Keogh, E. J., Tataw, O. M., & Souza, V. M. A. de. (2014). CID: an efficient complexity-invariant distance for time series. Data Mining and Knowledge Discovery, 28( 3), 634-669. doi:10.1007/s10618-013-0312-3
NLM
Batista GE de APA, Keogh EJ, Tataw OM, Souza VMA de. CID: an efficient complexity-invariant distance for time series [Internet]. Data Mining and Knowledge Discovery. 2014 ; 28( 3): 634-669.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-013-0312-3
Vancouver
Batista GE de APA, Keogh EJ, Tataw OM, Souza VMA de. CID: an efficient complexity-invariant distance for time series [Internet]. Data Mining and Knowledge Discovery. 2014 ; 28( 3): 634-669.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-013-0312-3
Artigo de periodico
A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies (2013)
Campello, Ricardo José Gabrielli Barreto; Moulavi, D; Zimek, A; Sander, J
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CAMPELLO, Ricardo José Gabrielli Barreto et al. A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies. Data Mining and Knowledge Discovery, v. 27, n. 3, p. 344-371, 2013Tradução . . Disponível em: https://doi.org/10.1007/s10618-013-0311-4. Acesso em: 03 jun. 2024.
APA
Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2013). A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies. Data Mining and Knowledge Discovery, 27( 3), 344-371. doi:10.1007/s10618-013-0311-4
NLM
Campello RJGB, Moulavi D, Zimek A, Sander J. A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies [Internet]. Data Mining and Knowledge Discovery. 2013 ; 27( 3): 344-371.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-013-0311-4
Vancouver
Campello RJGB, Moulavi D, Zimek A, Sander J. A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies [Internet]. Data Mining and Knowledge Discovery. 2013 ; 27( 3): 344-371.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-013-0311-4
Artigo de periodico
Cluster ensemble selection based on relative validity indexes (2013)
Naldi, M. C; Carvalho, André Carlos Ponce de Leon Ferreira de ; Campello, Ricardo José Gabrielli Barreto
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
NALDI, M. C e CARVALHO, André Carlos Ponce de Leon Ferreira de e CAMPELLO, Ricardo José Gabrielli Barreto. Cluster ensemble selection based on relative validity indexes. Data Mining and Knowledge Discovery, v. 27, n. 2, p. 259-289, 2013Tradução . . Disponível em: https://doi.org/10.1007/s10618-012-0290-x. Acesso em: 03 jun. 2024.
APA
Naldi, M. C., Carvalho, A. C. P. de L. F. de, & Campello, R. J. G. B. (2013). Cluster ensemble selection based on relative validity indexes. Data Mining and Knowledge Discovery, 27( 2), 259-289. doi:10.1007/s10618-012-0290-x
NLM
Naldi MC, Carvalho ACP de LF de, Campello RJGB. Cluster ensemble selection based on relative validity indexes [Internet]. Data Mining and Knowledge Discovery. 2013 ; 27( 2): 259-289.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-012-0290-x
Vancouver
Naldi MC, Carvalho ACP de LF de, Campello RJGB. Cluster ensemble selection based on relative validity indexes [Internet]. Data Mining and Knowledge Discovery. 2013 ; 27( 2): 259-289.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-012-0290-x
Artigo de periodico
A fast and effective method to find correlations among attributes in databases (2007)
Sousa, Elaine Parros Machado de; Traina Junior, Caetano; Traina, Agma Juci Machado; Wu, Leejay; Faloutsos, Christos
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Subjects: COMPUTAÇÃO GRÁFICA, PROCESSAMENTO DE IMAGENS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOUSA, Elaine Parros Machado de et al. A fast and effective method to find correlations among attributes in databases. Data Mining and Knowledge Discovery, v. 14, n. 3, p. 367-407, 2007Tradução . . Disponível em: https://doi.org/10.1007/s10618-006-0056-4. Acesso em: 03 jun. 2024.
APA
Sousa, E. P. M. de, Traina Junior, C., Traina, A. J. M., Wu, L., & Faloutsos, C. (2007). A fast and effective method to find correlations among attributes in databases. Data Mining and Knowledge Discovery, 14( 3), 367-407. doi:10.1007/s10618-006-0056-4
NLM
Sousa EPM de, Traina Junior C, Traina AJM, Wu L, Faloutsos C. A fast and effective method to find correlations among attributes in databases [Internet]. Data Mining and Knowledge Discovery. 2007 ; 14( 3): 367-407.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-006-0056-4
Vancouver
Sousa EPM de, Traina Junior C, Traina AJM, Wu L, Faloutsos C. A fast and effective method to find correlations among attributes in databases [Internet]. Data Mining and Knowledge Discovery. 2007 ; 14( 3): 367-407.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/s10618-006-0056-4

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