A First Attempt on Monotonic Training Set Selection

TitleA First Attempt on Monotonic Training Set Selection
Publication TypeConference Paper
Year of Publication2018
AuthorsCano, J. R., and García S.
EditorJuez, Francisco Javier de, Villar José Ramón, de la Cal Enrique A., Herrero Álvaro, Quintián Héctor, Sáez José António, and Corchado Emilio
Conference NameHybrid Artificial Intelligent Systems
PublisherSpringer International Publishing
Conference LocationCham
ISBN Number978-3-319-92639-1

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.