Lazy Multi-label Learning Algorithms Based on Mutuality Strategies

Autor(es)

Cherman, Everton Alvares ; Spolaor, Newton ; Valverde-Rebaza, Jorge ; Monard, Maria Carolina

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS; v. 80, p. 16-pg., 2015-12-01.

Resumo

Lazy multi-label learning algorithms have become an important research topic within the multi-label community. These algorithms usually consider the set of standard k-Nearest Neighbors of a new instance to predict its labels (multi-label). The prediction is made by following a voting criteria within the multi-labels of the set of k-Nearest Neighbors of the new instance. This work proposes the use of two alternative strategies to identify the set of these examples: the Mutual and Not Mutual Nearest Neighbors rules, which have already been used by lazy single-learning algorithms. In this work, we use these strategies to extend the lazy multi-label algorithm BRkNN. An experimental evaluation carried out to compare both mutuality strategies with the original BRkNN algorithm and the well-known MLkNN lazy algorithm on 15 benchmark datasets showed that MLkNN presented the best predictive performance for the Hamming-Loss evaluation measure, although it was significantly outperformed by the mutuality strategies when F-Measure is considered. The best results of the lazy algorithms were also compared with the results obtained by the Binary Relevance approach using three different base learning algorithms. (AU)

• Processo FAPESP: 13/12191-5 - Mineração do Comportamento de Usuários em Redes Sociais baseadas em Localização
• Beneficiário: Jorge Carlos Valverde Rebaza
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 10/15992-0 - Explorando a dependência de rótulos no aprendizado multirrótulo
• Beneficiário: Everton Alvares Cherman
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto
• Processo FAPESP: 11/02393-4 - Seleção de Atributos para Aprendizado Multirrótulo
• Beneficiário: Newton Spolaôr
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 11/22749-8 - Desafios em visualização exploratória de dados multidimensionais: novos paradigmas, escalabilidade e aplicações
• Beneficiário: Luis Gustavo Nonato
• Modalidade de apoio: Auxílio à Pesquisa - Temático