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

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4 Scopus citations

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.

Original languageEnglish
Pages (from-to)137-155
Number of pages19
JournalAnnals of Mathematics and Artificial Intelligence
Volume87
Issue number1-2
DOIs
StatePublished - Oct 1 2019

Bibliographical note

Funding Information:
The work of the second author was supported by the NSF grant IIS-1618783.

Funding Information:
The work of the second author was supported by the NSF grant IIS-1618783.

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Keywords

  • Computational complexity theory
  • Lexicographic preference models
  • Maximum satisfiability
  • Preference learning
  • Preference modeling and reasoning
  • Social choice theory
  • Voting theory

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics

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