A First Attempt on Monotonic Training Set Selection

Conference Paper 2018

José Ramón Cano De Amo, Salvador García López

Pages 277-288

Google Scholar All publications

Cite this publication

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.
BibTeX

Abstract

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.

Details

Editors
Francisco Juez, José Villar, Enrique de la Cal, álvaro Herrero, Héctor Quintián, José Sáez, Emilio Corchado
Publisher
Springer International Publishing
Conference Location
Cham
ISBN Number
978-3-319-92639-1