Binary ranking for ordinal class imbalance

• Tipo: Artigo em Revista Científica Internacional
• Data: 2018

Autores

Ricardo Cruz (Autor) Outra; Kelwin Fernandes (Autor) Outra; Joaquim F. Pinto Costa (Autor) FCUP; María Pérez Ortiz (Autor) Outra; Jaime S. Cardoso (Autor) FEUP

Revista

Título: Pattern Analysis and Applications

Vol. 21 Nº 4

Páginas: 931-939

ISSN: 1433-7541

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 5 Citações

Outras Informações

ID Authenticus: P-00N-VRD

DOI: 10.1007/s10044-018-0705-4

Abstract (EN): Imbalanced classification has been extensively researched in the last years due to its prevalence in real-world datasets, ranging from very different topics such as health care or fraud detection. This literature has long been dominated by variations of the same family of solutions (e.g. mainly resampling and cost-sensitive learning). Recently, a new and promising way of tackling this problem has been introduced: learning with scoring pairwise ranking so that each pair of classes contribute in tandem to the decision boundary. In this sense, the paper addresses the problem of class imbalance in the context of ordinal regression, proposing two novel contributions: (a) approaching the imbalance by binary pairwise ranking using a well-known label decomposition ensemble, and (b) introducing a regularization into this ensemble so that parallel decision boundaries are favored. These are two independent contributions that synergize well. Our model is tested using linear Support Vector Machines and our results are compared against state-of-the-art models. Both approaches show promising performance in ordinal class imbalance, with an overall 15% improvement relative to the state-of-the-art, as evaluated by a balanced metric.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9