Título Fine-to-Coarse Ranking in Ordinal and Imbalanced Domains: An Application to Liver Transplantation
Autores PÉREZ ORTIZ, MARÍA, Fernandes, Kelwin , Cruz, Ricardo , Cardoso, Jaime S. , Briceno, Javier , Hervas-Martinez, Cesar
Publicación externa No
Medio Lecture Notes in Computer Science
Alcance Proceedings Paper
Naturaleza Científica
Cuartil JCR 4
Cuartil SJR 2
Impacto SJR 0.295
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020922737&doi=10.1007%2f978-3-319-59147-6_45&partnerID=40&md5=1622bb000e6ebfcca35f3be2677a45b6
Fecha de publicacion 01/01/2017
ISI 000443108700045
Scopus Id 2-s2.0-85020922737
DOI 10.1007/978-3-319-59147-6_45
Abstract Nowadays imbalanced learning represents one of the most vividly discussed challenges in machine learning. In these scenarios, one or some of the classes in the problem have a significantly lower a priori probability, usually leading to trivial or non-desirable classifiers. Because of this, imbalanced learning has been researched to a great extent by means of different approaches. Recently, the focus has switched from binary classification to other paradigms where imbalanced data also arise, such as ordinal classification. This paper tests the application of learning pairwise ranking with multiple granularity levels in an ordinal and imbalanced classification problem where the aim is to construct an accurate model for donor-recipient allocation in liver transplantation. Our experiments show that approaching the problem as ranking solves the imbalance issue and leads to a competitive performance.
Palabras clave Imbalanced data; Ranking; Ordinal classification; Over-sampling
Miembros de la Universidad Loyola

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