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

• 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

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...