Cano De Amo, J. R. . ., Aljohani, N. R., Abbasi, R. A., Alowidbi, J. S., & García López, S. . (2017). Prototype selection to improve monotonic nearest neighbor. Engineering Applications of Artificial Intelligence, 60, 128-135. https://doi.org/10.1016/j.engappai.2017.02.006
Monotonic classification
Cano De Amo, J. R. . ., & García López, S. . (2017). Training set selection for monotonic ordinal classification. Data \& Knowledge Engineering, 112, 94-105. https://doi.org/10.1016/j.datak.2017.10.003
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
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)