Online Multi-label Classification with Adaptive Model Rules

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2016

Autores

Ricardo Sousa (Autor) Outra; João Gama (Autor) FEP

Ata de Conferência Internacional

Título: ADVANCES IN ARTIFICIAL INTELLIGENCE, CAEPIA 2016

Páginas: 58-67

17th Conference of the Spanish-Association-for-Artificial-Intelligence (CAEPIA)

Salamanca, SPAIN, SEP 14-16, 2016

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00K-VAY

DOI: 10.1007/978-3-319-44636-3_6

Abstract (EN): The interest on online classification has been increasing due to data streams systems growth and the need for Multi-label Classification applications have followed the same trend. However, most of classification methods are not performed on-line. Moreover, data streams produce huge amounts of data and the available processing resources may not be sufficient. This work-in-progress paper proposes an algorithm for Multi-label Classification applications in data streams scenarios. The proposed method is derived from multi-target structured regressor AMRules that produces models using subsets of output attributes (output specialization strategy). Performance tests were conducted where the operation modes global, local and subset approaches of the proposed method were compared to each other and to others online multi-label classifiers described in the literature. Three datasets of real scenarios were used for evaluation. The results indicate that the subset specialization mode is competitive in comparison to local and global approaches and to other online multi-label classifiers.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10