TY - GEN

T1 - Data representation and feature selection for colorimetric sensor arrays used as explosives detectors

A1 - Alstrøm,Tommy Sonne

A1 - Larsen,Jan

A1 - Kostesha,Natalie

A1 - Jakobsen,Mogens Havsteen

A1 - Boisen,Anja

AU - Alstrøm,Tommy Sonne

AU - Larsen,Jan

AU - Kostesha,Natalie

AU - Jakobsen,Mogens Havsteen

AU - Boisen,Anja

PB - IEEE

PY - 2011

Y1 - 2011

N2 - Within the framework of the strategic research project Xsense at the Technical University of Denmark, we are developing a colorimetric sensor array which can be useful for detection of explosives like DNT, TNT, HMX, RDX and TATP and identification of volatile organic compounds in the presence of water vapor in air. In order to analyze colorimetric sensors with statistical methods, the sensory output must be put into numerical form suitable for analysis. We present new ways of extracting features from a colorimetric sensor and determine the quality and robustness of these features using machine learning classifiers. Sensors, and in particular explosive sensors, must not only be able to classify explosives, they must also be able to measure the certainty of the classifier regarding the decision it has made. This means there is a need for classifiers that not only give a decision, but also give a posterior probability about the decision. We will compare K-nearest neighbor, artificial neural networks and sparse logistic regression for colorimetric sensor data analysis. Using the sparse solutions we perform feature selection and feature ranking and compare to Gram-Schmidt orthogonalization.

AB - Within the framework of the strategic research project Xsense at the Technical University of Denmark, we are developing a colorimetric sensor array which can be useful for detection of explosives like DNT, TNT, HMX, RDX and TATP and identification of volatile organic compounds in the presence of water vapor in air. In order to analyze colorimetric sensors with statistical methods, the sensory output must be put into numerical form suitable for analysis. We present new ways of extracting features from a colorimetric sensor and determine the quality and robustness of these features using machine learning classifiers. Sensors, and in particular explosive sensors, must not only be able to classify explosives, they must also be able to measure the certainty of the classifier regarding the decision it has made. This means there is a need for classifiers that not only give a decision, but also give a posterior probability about the decision. We will compare K-nearest neighbor, artificial neural networks and sparse logistic regression for colorimetric sensor data analysis. Using the sparse solutions we perform feature selection and feature ranking and compare to Gram-Schmidt orthogonalization.

UR - http://mlsp2011.conwiz.dk/

U2 - 10.1109/MLSP.2011.6064615

DO - 10.1109/MLSP.2011.6064615

SN - 978-1-4577-1621-8

BT - 2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

T2 - 2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

T3 - Uden navn

T3 - en_GB

ER -