Label Ranking Forests

• Tipo: Artigo em Revista Científica Internacional
• Data: 2017

Autores

de Sa, CR (Autor) Outra; Carlos Soares (Autor) FEUP; Knobbe, A (Autor) Outra; Cortez, P (Autor) Outra

Revista

Título: Expert Systems

Vol. 34

Página Final: e12166

ISSN: 0266-4720

Editora: Wiley-Blackwell

Indexação

ISI Web of Knowledge - 30 Citações

Scopus - 31 Citações

Outras Informações

ID Authenticus: P-00M-FN6

DOI: 10.1111/exsy.12166

Abstract (EN): The problem of Label Ranking is receiving increasing attention from several research communities. The algorithms that have been developed/adapted to treat rankings of a fixed set of labels as the target object, including several different types of decision trees (DT). One DT-based algorithm, which has been very successful in other tasks but which has not been adapted for label ranking is the Random Forests (RF) algorithm. RFs are an ensemble learning method that combines different trees obtained using different randomization techniques. In this work, we propose an ensemble of decision trees for Label Ranking, based on Random Forests, which we refer to as Label Ranking Forests (LRF). Two different algorithms that learn DT for label ranking are used to obtain the trees. We then compare and discuss the results of LRF with standalone decision tree approaches. The results indicate that the method is highly competitive.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8