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Designing Smart Homes pp 132–145Cite as

Application of Decision Trees to Smart Homes

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4008))

Abstract

This chapter aims to illustrate a possible way of using decision trees to make Smart Homes smarter. Decision trees are popular modelling technique, and the corresponding models are both predictive and descriptive. We formulate the modelling problem by defining the generic question “Is the undergoing activity or event in the Smart Home usual?” Then we explain how it is possible to gather appropriate data from the sensors and pre-process these data to form appropriate input for a decision tree algorithm. We further explain the mainstream approaches in decision trees algorithms rather then analysing them in detail, and we give short overview of available software. Finally, we explain some measures for quantitative and qualitative evaluation of the induced decision tree models (e.g. expert opinion, cross-validation, statistical tests etc.).

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References

  1. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  2. Kononenko, I.: Comparison of Inductive and Naive Bayesian Learning Approaches to Automatic Knowledge Acquisition. Current Trends in Knowledge Adquisition, 190–197 (1990)

    Google Scholar 

  3. Fausett, L.: Fundamentals of Neural Networks. Prentice-Hall, Englewood Cliffs (1994)

    MATH  Google Scholar 

  4. Murthy, S.K.: Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey. Data Mining and Knowledge Discovery 2(4), 345–389 (1998)

    Article  Google Scholar 

  5. Karimi, K., Hamilton, H.J.: Temporal Rules and Temporal Decision Trees: A C4.5 Approach. Technical Report CS-2001-02. Department of Computer Science, University of Regina (2001)

    Google Scholar 

  6. AMIGO: Ambient intelligence for the networked home environment, http://www.hitech-projects.com/euprojects/amigo/

  7. Mitchell, T.: Decision Tree Learning. In: Mitchell, T. (ed.) Machine Learning, pp. 52–78. The McGraw-Hill Companies, Inc, New York (1997)

    Google Scholar 

  8. Brdiczka, O., Reignier, P., James, L.: Crowley Supervised Learning of an Abstract Context Model for an Intelligent Environment, In: Joint sOc-EUSAI conference. Grenoble (2005)

    Google Scholar 

  9. Stankovski, V., Bratko, I., Demšar, J., Smrke, D.: Induction of Hypotheses Concerning Hip Arthroplasty: A Modified Methodology for Medical Research. Methods Inf. Med. 40, 392–396 (2001)

    Google Scholar 

  10. Stankovski, V., Debeljak, M., Bratko, I., Adamič, M.: Modelling the population dynamics of red deer (Cervus elaphus L.) with regard to forest development. Ecol. model 108(1-3), 145–153 (1998)

    Article  Google Scholar 

  11. Murthy, S.: On Growing Better Decision Trees from Data. PhD Thesis (1997)

    Google Scholar 

  12. Quinlan, R.: Top Down Induction of Decision Trees. Mach. Learn. 1, 81–106 (1986)

    Google Scholar 

  13. Myers, R., Walpole, R.: Tests of Hypotheses. In: Myers, R., Walpole, R. (eds.) Probability and Statistics for Engineers and Scientists, 2nd edn., pp. 268–273. Macmillan Publishing Co., Inc., New York (1978)

    Google Scholar 

  14. Hand, D.J.: Data mining: statistics and more. The American Statistician 52, 112–118 (1988)

    Article  Google Scholar 

  15. Winston, P.: Learning by Building Identification Trees. In: Winston, P. (ed.) Artificial Intelligence, pp. 423–442. Addison-Wesley Publishing Company, Reading (1992)

    Google Scholar 

  16. Brunk, C., Kelly, J., Kohavi, R.: MineSet: An Integrated System for Data Mining. In: Heckerman, D., et al. (eds.) Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pp. 135–138. AAAI Press, Menlo Park (1997)

    Google Scholar 

  17. Clementine, Web site: http://www.spss.com/clementine/

  18. Orange, Web site: http://magix.fri.uni-lj.si/orange/

  19. DataMiningGrid: Data Mining in Grid Computing Environments, Web site: http://www.datamininggrid.org/

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Author information

Authors and Affiliations

  1. Department of Construction Informatics, Faculty of Civil and Geodetic Engineering, University of Ljubljana, Jamova 2, SI-1000, Slovenia

    Vlado Stankovski & Jernej Trnkoczy

Authors
  1. Vlado Stankovski
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  2. Jernej Trnkoczy
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Editor information

Editors and Affiliations

  1. School of Computing and Mathematics, University of Ulster, UK

    Juan Carlos Augusto

  2. School of Computing and Mathematics and Computer Science Research Institute, University of Ulster, Northern Ireland

    Chris D. Nugent

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© 2006 Springer-Verlag Berlin Heidelberg

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Stankovski, V., Trnkoczy, J. (2006). Application of Decision Trees to Smart Homes. In: Augusto, J.C., Nugent, C.D. (eds) Designing Smart Homes. Lecture Notes in Computer Science(), vol 4008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11788485_8

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  • DOI: https://doi.org/10.1007/11788485_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35994-4

  • Online ISBN: 978-3-540-35995-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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