A First Attempt on Monotonic Training Set Selection

Comunicación en congreso 2018

José Ramón Cano De Amo, Salvador García López

Páginas 277-288

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Cómo citar esta publicación

Cano De Amo, J. R., & García López, S. (2018). A First Attempt on Monotonic Training Set Selection (F. J. de C. Juez, J. R. Villar, E. A. de la Cal, álvaro Herrero, H. Quintián, J. A. Sáez, & E. Corchado, Eds.). Cham: Springer International Publishing.
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Resumen

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.

Detalles

Editores
Francisco Juez, José Villar, Enrique de la Cal, álvaro Herrero, Héctor Quintián, José Sáez, Emilio Corchado
Editorial
Springer International Publishing
Lugar del congreso
Cham
ISBN
978-3-319-92639-1