Evolving Random Forest for Preference Learning

Mohamed Abou-Zleikha, Noor Shaker

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

5 Citations (Scopus)
462 Downloads (Pure)


This paper introduces a novel approach for pairwise preference learning through a combination of an evolutionary method and random forest. Grammatical evolution is used to describe the structure of the trees in the Random Forest (RF) and to handle the process of evolution. Evolved random forests are evaluated based on their efficiency in predicting reported preferences. The combination of these two efficient methods for evolution and modelling yields a powerful technique for learning pairwise preferences. To test the proposed methodology and compare it to other methods in the literature, a dataset of 1560 sessions with detail information about user behaviour and their self-reported preferences while interacting with a game is used for training and evaluation. The method demonstrates ability to construct accurate models of user experience from preferences, behavioural and context data. The results obtained for predicting pairwise self-reports of users for the three emotional states engagement, frustration and challenge show very promising results that are comparable and in some cases superior to those obtained from state-of-the-art methods.
Original languageEnglish
Title of host publicationApplications of Evolutionary Computation : 18th European Conference, EvoApplications 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings
EditorsAntonio M. Mora, Giovanni Squillero
Publication date2015
ISBN (Print)978-3-319-16548-6
ISBN (Electronic)978-3-319-16549-3
Publication statusPublished - 2015
Event18th Conference on the Applications of Evolutionary Computation 2015 - Copenhagen, Denmark
Duration: 8 Apr 201510 Apr 2015


Conference18th Conference on the Applications of Evolutionary Computation 2015
SeriesLecture Notes in Computer Science

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