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Evolving Random Forest for Preference Learning

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Applications of Evolutionary Computation (EvoApplications 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9028))

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Abstract

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.

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Acknowledgement

The research was supported in part by the Danish Research Agency, Ministry of Science, Technology and Innovation; project “PlayGALe” (1337-00172). This work also was supported in part by the Danish Council for Strategic Research of the Danish Agency for Science Technology and Innovation under the CoSound project, case number 11-115328. This publication only reflects the authors views. The authors would like to thank Prof. Georgios Yannakakis and Dr. Héctor P. Martinez for valuable discussions.

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Authors and Affiliations

  1. Audio Analysis Lab, AD:MT, Aalborg University, Aalborg, Denmark

    Mohamed Abou-Zleikha & Noor Shaker

  2. IT University of Copenhagen, Copenhagen, Denmark

    Mohamed Abou-Zleikha & Noor Shaker

Authors
  1. Mohamed Abou-Zleikha
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  2. Noor Shaker
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Corresponding author

Correspondence to Mohamed Abou-Zleikha .

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Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Antonio M. Mora

  2. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

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Abou-Zleikha, M., Shaker, N. (2015). Evolving Random Forest for Preference Learning. In: Mora, A., Squillero, G. (eds) Applications of Evolutionary Computation. EvoApplications 2015. Lecture Notes in Computer Science(), vol 9028. Springer, Cham. https://doi.org/10.1007/978-3-319-16549-3_26

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  • DOI: https://doi.org/10.1007/978-3-319-16549-3_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16548-6

  • Online ISBN: 978-3-319-16549-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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