Fine-to-Coarse Ranking in Ordinal and Imbalanced Domains: An Application to Liver Transplantation

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

Perez Ortiz, M (Autor) Outra; Fernandes, K (Autor) Outra; Cruz, R (Autor) Outra; Jaime S Cardoso (Autor) FEUP; Briceno, J (Autor) Outra; Hervas Martinez, C (Autor) Outra

Ata de Conferência Internacional

Título: ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT II

Páginas: 525-537

14th International Work-Conference on Artificial Neural Networks (IWANN)

Cadiz, SPAIN, JUN 14-16, 2017

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00N-9XT

DOI: 10.1007/978-3-319-59147-6_45

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13