TY - GEN
T1 - Non-Linear Machine Learning with Active Sampling for MOX Drift Compensation
AU - Matthews, Tamara
AU - Iqbal, Muhammad
AU - González-Vélez, Horacio
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Metal oxide (MOX) gas detectors based on SnO-2 provide low-cost solutions for real-time sensing of complex gas mixtures for indoor ambient monitoring. With high sensitivity under ideal conditions, MOX detectors may have poor long-term response accuracy due to environmental factors (humidity and temperature) along with sensor aging, leading to calibration drifts. Finding a simple and efficient solution to correct such calibration drifts has been the subject of numerous studies but remains an open problem. In this work, we present an efficient approach to MOX calibration using active and transfer sampling techniques coupled with non-linear machine learning algorithms, namely neural networks, extreme gradient boosting (XGBoost) and radial kernel support vector machines (SVM). Applied on the UCI's HT detectors dataset, the study evaluates methods for active sampling, makes an assessment of suitable neural networks architectures and compares the performance of neural networks, XGBoost and radial kernel SVM to classify gas mixtures (banana and wine odours, clean air) in the presence of humidity and temperature changes. The results show high classification accuracy levels (above 90%) and confirm that active sampling can provide a suitable solution.
AB - Metal oxide (MOX) gas detectors based on SnO-2 provide low-cost solutions for real-time sensing of complex gas mixtures for indoor ambient monitoring. With high sensitivity under ideal conditions, MOX detectors may have poor long-term response accuracy due to environmental factors (humidity and temperature) along with sensor aging, leading to calibration drifts. Finding a simple and efficient solution to correct such calibration drifts has been the subject of numerous studies but remains an open problem. In this work, we present an efficient approach to MOX calibration using active and transfer sampling techniques coupled with non-linear machine learning algorithms, namely neural networks, extreme gradient boosting (XGBoost) and radial kernel support vector machines (SVM). Applied on the UCI's HT detectors dataset, the study evaluates methods for active sampling, makes an assessment of suitable neural networks architectures and compares the performance of neural networks, XGBoost and radial kernel SVM to classify gas mixtures (banana and wine odours, clean air) in the presence of humidity and temperature changes. The results show high classification accuracy levels (above 90%) and confirm that active sampling can provide a suitable solution.
KW - Extreme Gradient Boosting
KW - Machine Learning
KW - Neural Networks
KW - Non-Linear Learning Methods
KW - XGBoost
UR - http://www.scopus.com/inward/record.url?scp=85061778794&partnerID=8YFLogxK
U2 - 10.1109/BDCAT.2018.00016
DO - 10.1109/BDCAT.2018.00016
M3 - Conference contribution
AN - SCOPUS:85061778794
T3 - Proceedings - 5th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2018
SP - 61
EP - 70
BT - Proceedings - 5th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2018
A2 - Sill, Alan
A2 - Spillner, Josef
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2018
Y2 - 17 December 2018 through 20 December 2018
ER -