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.
|Title of host publication||Intelligent Distributed Computing VIII, 3-5 September 2014, Madrid, Spain|
|Editors||D. Camacho, L. Braubach, S. Venticinque, C. Badica|
|Place of Publication||Cham|
|Publication status||Published - 2015|
|Name||Studies in Computational Intelligence|
Kota Gopalakrishna, A., Özçelebi, T., Liotta, A., & Lukkien, J. J. (2015). Statistical inference for intelligent lighting : a pilot study. In D. Camacho, L. Braubach, S. Venticinque, & C. Badica (Eds.), Intelligent Distributed Computing VIII, 3-5 September 2014, Madrid, Spain (pp. 9-18). (Studies in Computational Intelligence; Vol. 570). Springer. https://doi.org/10.1007/978-3-319-10422-5_3