Abstract
The decision process in the design and implementation of intelligent lighting applications benefits from insights about the data collected and a deep understanding of the relations among its variables. Data analysis using machine learning allows discovery of knowledge for predictive purposes. In this paper, we analyze a dataset collected on a pilot intelligent lighting application (the breakout dataset) using a supervised machine learning based approach. The performance of the learning algorithms is evaluated using two metrics: Classification Accuracy (CA) and Relevance Score (RS). We find that the breakout dataset has a predominant one-tomany relationship, i.e. a given input may have more than one possible output and that RS is an appropriate metric as opposed to the commonly used CA.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Analyzing and Interpreting Data. Technical report, Wilder Research (2009)
Clarke, P., Niblett, T.: The CN2 Rule Induction Algorithm. In: Machine Learning, pp. 261–283 (1989)
Cohen, W.H.: Fast Effective Rule Induction. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 115–123. Morgan Kaufmann (1995)
Frank, E., Witten, I.H.: Generating Accurate Rule Sets Without Global Optimization. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 144–151 (1998)
Gaines, B.R., Compton, P.: Induction of ripple-down rules applied to modeling large databases. Journal of Intelligent Information Systems 5(3), 211–228 (1995)
Gopalakrishna, A.K., Ozcelebi, T., Liotta, A., Lukkien, J.J.: Exploiting Machine Learning for Intelligent Room Lighting Applications. In: Proceedings of the 6th IEEE International Conference on Intelligent Systems, pp. 406–411 (2012)
Gopalakrishna, A.K., Ozcelebi, T., Liotta, A., Lukkien, J.J.: Relevance as a Metric for Evaluating Machine Learning Algorithms. In: Perner, P. (ed.) MLDM 2013. LNCS (LNAI), vol. 7988, pp. 195–208. Springer, Heidelberg (2013)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)
Kohavi, R.: The Power of Decision Tables. In: Lavrač, N., Wrobel, S. (eds.) ECML 1995. LNCS, vol. 912, pp. 174–189. Springer, Heidelberg (1995)
Liotta, A.: The Cognitive Net is Coming. IEEE Spectrum 50(8), 26–31 (2013)
Martin, B.: Instance-based Learning: Nearest Neighbor with Generalization. Technical Report, University of Waikato (1995)
Offermans, S., van Essen, H., Eggen, B.: Exploring a Hybrid Control Approach for enhanced User Experience of Interactive Lighting. In: Proceedings of the 27th International BCS Human Computer Interaction Conference, pp. 1–9 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Gopalakrishna, A.K., Ozcelebi, T., Liotta, A., Lukkien, J.J. (2015). Statistical Inference for Intelligent Lighting: A Pilot Study. In: Camacho, D., Braubach, L., Venticinque, S., Badica, C. (eds) Intelligent Distributed Computing VIII. Studies in Computational Intelligence, vol 570. Springer, Cham. https://doi.org/10.1007/978-3-319-10422-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-10422-5_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10421-8
Online ISBN: 978-3-319-10422-5
eBook Packages: EngineeringEngineering (R0)