Name
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-to-many 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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10422-5_3 | Studies in Computational Intelligence |
Field | DocType | Volume |
Intelligent lighting,Computer science,Control engineering,Statistical inference,Artificial intelligence,Decision process,Machine learning,Breakout | Conference | 570.0 |
ISSN | Citations | PageRank |
1860-949X | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aravind Kota Gopalakrishna | 1 | 38 | 4.55 |
| Tanir Ozcelebi | 2 | 148 | 24.48 |
| Antonio Liotta | 3 | 9 | 2.45 |
| Johan J. Lukkien | 4 | 671 | 70.50 |