López, V. ., Triguero, I. ., Carmona, C. J. ., García López, S. ., & Herrera Triguero, F. . (2014). Addressing Imbalanced Classification with Instance Generation Techniques: IPADE-ID. Neurocomputing, 126, 15-28. https://doi.org/10.1016/j.neucom.2013.01.050
Francisco Herrera Triguero
First name
Francisco
Last name
Herrera Triguero
2014
Fernández, A. ., del Río, S. ., López, V. ., Bawakid, A. ., del Jesus Díaz, M. J. ., Benitez, J. M., & Herrera Triguero, F. . (2014). Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks. WIREs Data Mining and Knowledge Discovery, 4, 380-409. https://doi.org/10.1002/widm.1134
Charte Ojeda, F. ., Rivera Rivas, A. J. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2014). Concurrence among Imbalanced Labels and Its Influence on Multilabel Resampling Algorithms. 110-121. Salamanca (Spain). https://doi.org/10.1007/978-3-319-07617-1_10 (Original work published)
Charte Ojeda, F. ., Rivera Rivas, A. J. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2014). LI-MLC: A Label Inference Methodology for Addressing High Dimensionality in the Label Space for Multilabel Classification. IEEE Transactions on Neural Networks and Learning Systems, 25, 1842-1854. https://doi.org/10.1109/TNNLS.2013.2296501
Gacto, M. J., Galende, M. ., Alcalá, R. ., & Herrera Triguero, F. . (2014). METSK-HDe: A Multiobjective Evolutionary Algorithm to learn accurate TSK-fuzzy Systems in High-Dimensional and Large-Scale Regression Problems. Information Sciences, 276, 63-79. https://doi.org/10.1016/j.ins.2014.02.047
Charte Ojeda, F. ., Rivera Rivas, A. J. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2014). MLeNN: A First Approach to Heuristic Multilabel Undersampling. 1-9. Salamanca (Spain). https://doi.org/10.1007/978-3-319-10840-7_1 (Original work published)
Carmona, C. J. ., González García, P. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2014). Overview on evolutionary subgroup discovery: analysis of the suitability and potential of the search performed by evolutionary algorithms. WIREs Data Mining and Knowledge Discovery, 4, 87-103. https://doi.org/10.1002/widm.1118
2013
Charte Ojeda, F. ., Rivera Rivas, A. J. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2013). A First Approach to Deal with Imbalance in Multi-label Datasets. 150-160. Salamanca (Spain). https://doi.org/10.1007/978-3-642-40846-5_16 (Original work published)
López, V. ., Fernández, A. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2013). A hierarchical genetic fuzzy system based on genetic programming for addressing classification with highly imbalanced and borderline data-sets. Knowledge-Based Systems, 38, 85-104. https://doi.org/10.1016/j.knosys.2012.08.025
Fernández, A. ., López, V. ., Galar, M. ., del Jesus Díaz, M. J. ., Herrera Triguero, F. ., & BV, E. S. (2013). Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches. Knowledge-Based Systems, 42, 97-110. https://doi.org/10.1016/j.knosys.2013.01.018