Multi-label classification from high-speed data streams with adaptive model rules and random rules

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

Autores

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

Revista

Título: Progress in Artificial Intelligence

Vol. 7

Páginas: 177-187

ISSN: 2192-6352

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 32 Citações

Outras Informações

ID Authenticus: P-00N-KHC

DOI: 10.1007/s13748-018-0142-z

Abstract (EN): Multi-label classification is a methodology that tries to solve classification problems where multiple classes are associated with each data example. Data streams pose new challenges to this methodology caused by the massive amounts of structured data production. In fact, most of the existent batch mode methods may not support this condition. Therefore, this paper proposes two multi-label classification methods based on rule and ensembles learning from continuous flow of data. These methods are derived from a multi-target regression algorithm. The main contribution of this work is the rule specialization for subsets of class labels, instead of the usual local (individual models for each output) or a global (one model for all outputs) methods. Prequential evaluation was conducted where global, local and subset operation modes were compared against other online classifiers found in the literature. Six real-world data sets were used. The evaluation demonstrated that the subset specialization presents competitive performance, when compared to local and global approaches and online classifiers found in the literature.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11