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
Gacto, M. J., Galende, M., Alcalá, R., & Herrera Triguero, F. (2013). Obtaining accurate TSK Fuzzy Rule-Based Systems by Multi-Objective Evolutionary Learning in high-dimensional regression problems. 1-7. (Original work published 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