Monotonicity constraints frequently appear in real-life problems. Many of the monotonic classifiers used in these cases require that the input data satisfy the monotonicity restrictions. This contribution proposes the use of training set selection to choose the most representative instances which improves the monotonic classifiers performance, fulfilling the monotonic constraints. We have developed an experiment on 30 data sets in order to demonstrate the benefits of our proposal.
CITATION STYLE
Cano, J. R., & García, S. (2018). A first attempt on monotonic training set selection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10870 LNAI, pp. 277–288). Springer Verlag. https://doi.org/10.1007/978-3-319-92639-1_23
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