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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Vydané v:	Annals of mathematics and artificial intelligence Ročník 87; číslo 1-2; s. 137 - 155
Hlavní autori:	Liu, Xudong, Truszczynski, Miroslaw
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 01.10.2019 Springer Springer Nature B.V
Predmet:	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
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