Statistical inference for intelligent lighting : a pilot study

A. Kota Gopalakrishna, T. Özçelebi, A. Liotta, J.J. Lukkien

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)
1 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationIntelligent Distributed Computing VIII, 3-5 September 2014, Madrid, Spain
EditorsD. Camacho, L. Braubach, S. Venticinque, C. Badica
Place of PublicationCham
ISBN (Print)978-3-319-10422-5
Publication statusPublished - 2015

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


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