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International Conference on Adaptive and Intelligent Systems

ICAIS 2014: Adaptive and Intelligent Systems pp 182–191Cite as

Multi-resident Activity Recognition Using Incremental Decision Trees

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8779))

Abstract

The present paper proposes the application of decision trees to model activities of daily living in a multi-resident context. An extension of ID5R, called E-ID5R, is proposed. It augments the leaf nodes and allows such nodes to be multi-labeled. E-ID5R induces a decision tree incrementally to accommodate new instances and new activities as they become available over time. To evaluate the proposed algorithm, the ARAS dataset which is a real-world multi-resident dataset stemming from two houses is used. E-ID5R performs differently on activities of both houses.

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Author information

Authors and Affiliations

  1. Department of Engineering and IT, Carinthia University of Applied Sciences, Klagenfurt, Austria

    Markus Prossegger

  2. Faculty of Science and Technology, Bournemouth University, Bournemouth, United Kingdom

    Abdelhamid Bouchachia

Authors
  1. Markus Prossegger
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  2. Abdelhamid Bouchachia
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Editor information

Editors and Affiliations

  1. Faculty of Science and Technology, Bournemouth University, Fern Barrow, BH12 5BB, Poole, Dorset, UK

    Abdelhamid Bouchachia

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© 2014 Springer International Publishing Switzerland

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Prossegger, M., Bouchachia, A. (2014). Multi-resident Activity Recognition Using Incremental Decision Trees. In: Bouchachia, A. (eds) Adaptive and Intelligent Systems. ICAIS 2014. Lecture Notes in Computer Science(), vol 8779. Springer, Cham. https://doi.org/10.1007/978-3-319-11298-5_19

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  • DOI: https://doi.org/10.1007/978-3-319-11298-5_19

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11297-8

  • Online ISBN: 978-3-319-11298-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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