Voting-based ensemble learning for partial lexicographic preference forests over combinatorial domains
| Autor: | Miroslaw Truszczynski, Xudong Liu |
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| Rok vydání: | 2019 |
| Předmět: |
Theoretical computer science
Computer science Applied Mathematics media_common.quotation_subject Complex system Decision tree 02 engineering and technology Lexicographical order Ensemble learning Rendering (computer graphics) Artificial Intelligence Voting 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Preference relation media_common |
| Zdroj: | Annals of Mathematics and Artificial Intelligence. 87:137-155 |
| ISSN: | 1573-7470 1012-2443 |
| DOI: | 10.1007/s10472-019-09645-7 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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