Abstract
Development of sensors and systems for detection of chemical compounds is an important challenge with applications in areas such as anti-terrorism, demining, and environmental monitoring. A newly developed colorimetric sensor array is able to detect explosives and volatile organic compounds; however, each sensor reading consists of hundreds of pixel values, and methods for combining these readings from multiple sensors must be developed to make a classification system. In this work we examine two distance based classification methods, K-Nearest Neighbor (KNN) and Gaussian process (GP) classification, which both rely on a suitable distance metric. We evaluate a range of different distance measures and propose a method for sensor fusion in the GP classifier. Our results indicate that the best choice of distance measure depends on the sensor and the chemical of interest.
Original language | English |
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Title of host publication | 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) |
Number of pages | 6 |
Place of Publication | 978-1-4673-1025-3 |
Publisher | IEEE |
Publication date | 2012 |
ISBN (Print) | 978-1-4673-1024-6 |
DOIs | |
Publication status | Published - 2012 |
Event | 2012 IEEE International Workshop on Machine Learning for Signal Processing - Santander, Spain Duration: 23 Oct 2012 → 26 Oct 2012 Conference number: 22 https://ieeexplore.ieee.org/xpl/conhome/6335571/proceeding |
Conference
Conference | 2012 IEEE International Workshop on Machine Learning for Signal Processing |
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Number | 22 |
Country/Territory | Spain |
City | Santander |
Period | 23/10/2012 → 26/10/2012 |
Internet address |
Series | Machine Learning for Signal Processing |
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ISSN | 1551-2541 |
Keywords
- Hausdorff distance
- Hellinger distance
- Chemo–selective compounds
- Feature extraction
- K–nearest neighbor classification
- Gaussian Process Classification