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

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 t...

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Bibliographic Details
Published in:Annals of mathematics and artificial intelligence Vol. 87; no. 1-2; pp. 137 - 155
Main Authors: Liu, Xudong, Truszczynski, Miroslaw
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.10.2019
Springer
Springer Nature B.V
Subjects:
Accuracy
Analysis
Artificial Intelligence
Cognition & reasoning
Combinatorial analysis
Complex Systems
Computer Science
Decision making
Decision theory
Decision trees
Ensemble learning
Forests and forestry
Majority rule
Mathematics
Optimization
Preferences
Voting
Voting theory
Maximum satisfiability
Preference modeling and reasoning
Preference learning
Computational complexity theory
68T30
Lexicographic preference models
Social choice theory
ISSN:1012-2443, 1573-7470
Online Access:Get full text
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Summary: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.
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ISSN:1012-2443
1573-7470
DOI:10.1007/s10472-019-09645-7