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de Haro-García, Aida, Cerruela-García, Gonzalo, García-Pedrajas, Nicolás (2019) Instance selection based on boosting for instance-based learners. Pattern Recognition, 96. 106959pp. doi:10.1016/j.patcog.2019.07.004

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Reference TypeJournal (article/letter/editorial)
TitleInstance selection based on boosting for instance-based learners
JournalPattern Recognition
Authorsde Haro-García, AidaAuthor
Cerruela-García, GonzaloAuthor
García-Pedrajas, NicolásAuthor
Year2019 (December)Volume96
PublisherElsevier BV
DOIdoi:10.1016/j.patcog.2019.07.004Search in ResearchGate
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Mindat Ref. ID9367918Long-form Identifiermindat:1:5:9367918:9
GUID0
Full Referencede Haro-García, Aida, Cerruela-García, Gonzalo, García-Pedrajas, Nicolás (2019) Instance selection based on boosting for instance-based learners. Pattern Recognition, 96. 106959pp. doi:10.1016/j.patcog.2019.07.004
Plain Textde Haro-García, Aida, Cerruela-García, Gonzalo, García-Pedrajas, Nicolás (2019) Instance selection based on boosting for instance-based learners. Pattern Recognition, 96. 106959pp. doi:10.1016/j.patcog.2019.07.004
In(2019) Pattern Recognition Vol. 96. Elsevier BV

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García-Pedrajas, Nicolás, del Castillo, Juan A. Romero, Cerruela-García, Gonzalo (2021) SI(FS)2: Fast simultaneous instance and feature selection for datasets with many features. Pattern Recognition, 111. 107723pp. doi:10.1016/j.patcog.2020.107723
García-Pedrajas, Nicolás E., Cuevas-Muñoz, José M., Cerruela-García, Gonzalo, de Haro-García, Aida (2024) A thorough experimental comparison of multilabel methods for classification performance. Pattern Recognition, 151. 110342 doi:10.1016/j.patcog.2024.110342
García-Pedrajas, Nicolás, de Haro-García, Aida, Pérez-Rodríguez, Javier (2013) A scalable approach to simultaneous evolutionary instance and feature selection. Information Sciences, 228. 150-174 doi:10.1016/j.ins.2012.10.006
García-Pedrajas, Nicolás E., Cuevas-Muñoz, José M., Cerruela-García, Gonzalo, de Haro-García, Aida (2024) Extensive experimental comparison among multilabel methods focused on ranking performance. Information Sciences, 679. doi:10.1016/j.ins.2024.121074
de Haro-García, Aida, Cerruela-García, Gonzalo, García-Pedrajas, Nicolás (2020) Ensembles of feature selectors for dealing with class-imbalanced datasets: A proposal and comparative study. Information Sciences, 540. 89-116 doi:10.1016/j.ins.2020.05.077
de Haro-García, Aida, García-Pedrajas, Nicolás (2009) A divide-and-conquer recursive approach for scaling up instance selection algorithms. Data Mining and Knowledge Discovery, 18. 392-418 doi:10.1007/s10618-008-0121-2
García-Pedrajas, Nicolás (2009) Supervised projection approach for boosting classifiers. Pattern Recognition, 42 (9). 1742-1760 doi:10.1016/j.patcog.2008.12.023
Cerruela-García, Gonzalo, Cuevas-Muñoz, José Manuel, García-Pedrajas, Nicolás (2022) Graph-Based Feature Selection Approach for Molecular Activity Prediction. Journal of Chemical Information and Modeling, 62 (7) 1618-1632 doi:10.1021/acs.jcim.1c01578
Derrac, Joaquín, García, Salvador, Herrera, Francisco (2010) IFS-CoCo: Instance and feature selection based on cooperative coevolution with nearest neighbor rule. Pattern Recognition, 43 (6). 2082-2105 doi:10.1016/j.patcog.2009.12.012
García-Pedrajas, Nicolás, Ortiz-Boyer, Domingo (2007) A cooperative constructive method for neural networks for pattern recognition. Pattern Recognition, 40 (1). 80-98 doi:10.1016/j.patcog.2006.06.024
Antelo-Collado, Aurelio, Carrasco-Velar, Ramón, García-Pedrajas, Nicolás, Cerruela-García, Gonzalo (2021) Effective Feature Selection Method for Class-Imbalance Datasets Applied to Chemical Toxicity Prediction. Journal of Chemical Information and Modeling, 61 (1) 76-94 doi:10.1021/acs.jcim.0c00908
 
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