Label Expansion for Multi-Label Classification

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2018

Autores

Adriano Rivolli (Autor) Outra; Carlos Soares (Autor) FEUP; André C. P. L. F. de Carvalho (Autor) Outra

Ata de Conferência Internacional

Título: 2018 7TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS)

Páginas: 414-419

7th Brazilian Conference on Intelligent Systems (BRACIS)

IBM Res, Sao Paulo, BRAZIL, OCT 22-25, 2018

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

INSPEC

Outras Informações

ID Authenticus: P-00Q-2M8

DOI: 10.1109/bracis.2018.00078

Abstract (EN): In multi-label classification tasks, instances are simultaneously associated with multiple labels, representing different and, possibly, related concepts from a domain. One characteristic of these tasks is a high class-label imbalance. In order to obtain improved predictive models, several algorithms either have explored the label dependencies or have dealt with the problem of imbalanced labels. This work proposes a label expansion approach which combines both alternatives. For such, some labels are expanded with data from a related class label, making the labels more balanced and representative. Preliminary experiments show the effectiveness of this approach to improve the Binary Relevance strategy. Particularly, it reduced the number of labels that were never predicted in the test instances. Although the results are preliminary, they are potentially attractive, considering the scale and consistency of the improvement obtained, as well as the broad scope of the proposed approach.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6