Comparison among different algorithms in classifying explosives using OFETs

Vapour phase detection of explosives using pattern recognition approaches is a very important area of research worldwide. This paper elaborates on the comparison between different algorithms in classifying empirical multiparametric data that are obtained from the explosive vapor sensors based on organic field effect transistors (OFETs). We address the problem of classification by means of statistical comparison among algorithms such as NaiveBayes (NBS), locally weighted learning (LWL), sequential minimal optimization (SMO) and J48 decision tree on data acquired from OFETs. This analysis helps in understanding the nature of data obtained from experiments and in making efficient estimators for the detection of explosives. The correctly classified instances for predicting tested samples using LWL, NBS, SMO and J48 decision tree are 72%, 73%, 80% and 90%, respectively. The future development of standoff explosive detectors will be benefited greatly by a proper choice of these classification approaches.