Title

Voting-based ensemble learning for partial lexicographic preference forests over combinatorial domains

Document Type

Article

Publication Date

10-1-2019

Subject Area

ARRAY(0x5598b56642d0)

Abstract

We study preference representation models based on partial lexicographic preference trees (PLP-trees). We propose to represent preference relations as forests of small PLP-trees (PLP-forests), and to use voting rules to aggregate orders represented by the individual trees into a single order to be taken as a model of the agent’s preference relation. We show that when learned from examples, PLP-forests have better accuracy than single PLP-trees. We also show that the choice of a voting rule does not have a major effect on the aggregated order, thus rendering the problem of selecting the “right” rule less critical. Next, for the proposed PLP-forest preference models, we develop methods to compute optimal and near-optimal outcomes, the tasks that appear difficult for some other common preference models. Lastly, we compare our models with those based on decision trees, which brings up questions for future research.

Publication Title

Annals of Mathematics and Artificial Intelligence

Volume

87

Issue

1-2

First Page

137

Last Page

155

Digital Object Identifier (DOI)

10.1007/s10472-019-09645-7

ISSN

10122443

E-ISSN

15737470

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