TY - GEN
T1 - Evolutionary strategy (ES) to optimize electronic nose sensor selection
AU - Li, Changying
AU - Heinemann, Paul
AU - Reed, Patrick
PY - 2006
Y1 - 2006
N2 - The high dimensionality of electronic nose data increases the difficulty of their use in classification models. Reducing this high dimensionality helps reduce variable numbers, improve classification accuracy, and reduce computation time and sensor cost. In this research, the Cyranose 320 electronic nose, which was used for apple defect detection, was optimized by selecting only the most relevant of its internal 32 sensors using different selection methods. The covariance matrix adaptation evolutionary strategy (CMAES), was applied and the average classification error rate of CMA over 30 random seed runs was 5.0% (s.d.=0.006). This study provided a robust and efficient optimization approach to reduce high data dimensionality of the electronic nose data, which substantially improved electronic nose performance in apple defect detection while potentially reducing the overall electronic nose cost for future specific applications.
AB - The high dimensionality of electronic nose data increases the difficulty of their use in classification models. Reducing this high dimensionality helps reduce variable numbers, improve classification accuracy, and reduce computation time and sensor cost. In this research, the Cyranose 320 electronic nose, which was used for apple defect detection, was optimized by selecting only the most relevant of its internal 32 sensors using different selection methods. The covariance matrix adaptation evolutionary strategy (CMAES), was applied and the average classification error rate of CMA over 30 random seed runs was 5.0% (s.d.=0.006). This study provided a robust and efficient optimization approach to reduce high data dimensionality of the electronic nose data, which substantially improved electronic nose performance in apple defect detection while potentially reducing the overall electronic nose cost for future specific applications.
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M3 - Conference contribution
AN - SCOPUS:58249117660
SN - 1892769557
SN - 9781892769558
T3 - Computers in Agriculture and Natural Resources - Proceedings of the 4th World Congress
SP - 321
EP - 328
BT - Computers in Agriculture and Natural Resources - Proceedings of the 4th World Congress
T2 - 4th World Congress on Computers in Agriculture and Natural Resources
Y2 - 24 July 2006 through 26 July 2006
ER -