@misc{10481/92477,
year = {2024},
month = {3},
url = {https://hdl.handle.net/10481/92477},
abstract = {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.},
organization = {Funding for open access publishing: Universidad de Granada/CBUA.},
organization = {Projects PID2020-119478GB-I00 and A-TIC-434-UGR20, and by a research scholarship (FPU18/05989), by the Spanish
Ministry of Science, Innovation and Universities},
publisher = {Springer Nature},
keywords = {Distance metric learning},
keywords = {Monotonic classification},
keywords = {Nearest neighbors},
title = {Metric learning for monotonic classification: turning the space up to the limits ofmonotonicity},
doi = {10.1007/s10489-024-05371-8},
author = {Suárez, Juan Luis and González Almagro, Germán and García López, Salvador and Herrera Triguero, Francisco},
}