Metric learning for monotonic classification: turning the space up to the limits ofmonotonicity Suárez, Juan Luis González Almagro, Germán García López, Salvador Herrera Triguero, Francisco Distance metric learning Monotonic classification Nearest neighbors This paper presents, for the first time, a distance metric learning algorithm for monotonic classification. Monotonic datasets arise in many real-world applications, where there exist order relations in the input and output variables, and the outputs corresponding to ordered pairs of inputs are also expected to be ordered. Monotonic classification can be addressed through several distance-based classifiers that are able to respect themonotonicity constraints of the data. The performance of distancebased classifiers can be improved with the use of distance metric learning algorithms, which are able to find the distances that best represent the similarities among each pair of data samples. However, learning a distance for monotonic data has an additional drawback: the learned distance may negatively impact the monotonic constraints of the data. In our work, we propose a new model for learning distances that does not corrupt these constraints. This methodology will also be useful in identifying and discarding non-monotonic pairs of samples that may be present in the data due to noise. The experimental analysis conducted, supported by a Bayesian statistical testing, demonstrates that the distances obtained by the proposed method can enhance the performance of several distance-based classifiers in monotonic problems. 2024-06-11T07:12:49Z 2024-06-11T07:12:49Z 2024-03-26 journal article Suárez, J.L., González-Almagro, G., García, S. et al. Metric learning for monotonic classification: turning the space up to the limits of monotonicity. Appl Intell 54, 4443–4466 (2024). https://doi.org/10.1007/s10489-024-05371-8 https://hdl.handle.net/10481/92477 10.1007/s10489-024-05371-8 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Springer Nature