Learning and Ensembling Lexicographic Preference Trees with Multiple Kernels

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

Autores

Fernandes, K (Autor) Outra; Jaime S Cardoso (Autor) FEUP; Palacios, H (Autor) Outra

Ata de Conferência Internacional

Título: 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)

Páginas: 2140-2147

International Joint Conference on Neural Networks (IJCNN)

Vancouver, CANADA, JUL 24-29, 2016

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00M-R08

DOI: 10.1109/ijcnn.2016.7727464

Abstract (EN): We study the problem of learning lexicographic preferences on multiattribute domains, and propose Rankdom Forests as a compact way to express preferences in learning to rank scenarios. We start generalizing Conditional Lexicographic Preference Trees by introducing multiple kernels in order to handle non-categorical attributes. Then, we define a learning strategy for inferring lexicographic rankers from partial pairwise comparisons between options. Finally, a Lexicographic Ensemble is introduced to handle multiple weak partial rankers, being Rankdom Forests one of these ensembles. We tested the performance of the proposed method using several datasets and obtained competitive results when compared with other lexicographic rankers.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8