Learning and Ensembling Lexicographic Preference Trees with Multiple Kernels

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Fernandes, K (Author) Other; Jaime S Cardoso (Author) FEUP; Palacios, H (Author) Other

Conference proceedings International

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

Pages: 2140-2147

International Joint Conference on Neural Networks (IJCNN)

Vancouver, CANADA, JUL 24-29, 2016

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 3 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8