Gonzalez, M. ., González-Almagro, G. ., Triguero, I. ., Cano De Amo, J. R. . ., & García López, S. . (2021). Decomposition-Fusion for Label Distribution Learning. Information Fusion, 66, 64-75. https://doi.org/10.1016/j.inffus.2020.08.024 (Original work published 2021)
Salvador García López
First name
Salvador
Last name
García López
2021
González-Almagro, G. ., Luengo, J. ., Cano De Amo, J. R. . ., & García López, S. . (2021). Enhancing instance-level constrained clustering through differential evolution. Applied Soft Computing, 108, 1-19. https://doi.org/10.1016/j.asoc.2021.107435
Gonzalez, M. ., Luengo, J. ., Cano De Amo, J. R. . ., & García López, S. . (2021). Synthetic Sample Generation for Label Distribution Learning. Information Sciences, 544, 197-213. https://doi.org/10.1016/j.ins.2020.07.071 (Original work published 2021)
2020
González-Almagro, G. ., Suarez, J. L., Luengo, J. ., Cano De Amo, J. R. . ., & García López, S. . (2020). Agglomerative Constrained Clustering Through Similarity and Distance Recalculation. 424-436. https://doi.org/10.1007/978-3-030-61705-9_35
González-Almagro, G. ., Luengo, J. ., Cano De Amo, J. R. . ., & García López, S. . (2020). DILS: Constrained clustering through dual iterative local search. Computers \& Operations Research, 121, 104979. https://doi.org/10.1016/j.cor.2020.104979
González-Almagro, G. ., Rosales-Pérez, A. ., Luengo, J. ., Cano De Amo, J. R. . ., & García López, S. . (2020). Improving constrained clustering via decomposition-based multiobjective optimization with memetic elitism. GECCO ’20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 333-341. https://doi.org/10.1145/3377930.3390187 (Original work published 2020)
Gonzalez, M. ., Cano De Amo, J. R. . ., & García López, S. . (2020). ProLSFEO-LDL: Prototype Selection and Label- Specific Feature Evolutionary Optimization for Label Distribution Learning. Applied Sciences, 10, 3089. https://doi.org/10.3390/app10093089
Cano De Amo, J. R. . ., Luengo, J. ., & García López, S. . (2020). Similarity-based and Iterative Label Noise Filters for Monotonic Classification. Proceedings of the 53rd Hawaii International Conference on System Sciences, 1698-1706. https://doi.org/10.24251/HICSS.2020.210
2019
Cano De Amo, J. R. . ., Luengo, J. ., & García López, S. . (2019). Label noise filtering techniques to improve monotonic classification. Neurocomputing, 353, 83-95. https://doi.org/10.1016/j.neucom.2018.05.131 (Original work published 2019)
Cano De Amo, J. R. . ., Gutiérrez, P. A., Krawczyk, B. ., Woźniak, M. ., & García López, S. . (2019). Monotonic classification: An overview on algorithms, performance measures and data sets. Neurocomputing, 341, 168-182. https://doi.org/10.1016/j.neucom.2019.02.024 (Original work published 2019)