Pattern analysis in illicit heroin seizures: a novel application of machine learning algorithms
نویسندگان
چکیده
An application of machine learning algorithms to the clustering and classification of chemical data concerning heroin seizures is presented. The data concerns the chemical constituents of heroin as given by a gas chromatography analysis. Following a preprocessing step, where the six initial constituents are reduced to only two significant features, the data are clustered in order to find natural classes which we have supposed to correspond to the country of origin. A classification is then made using a multi-layer perceptron, a probabilistic neural network, a radial basis function network and the k -nearest neighbors method. Results are encouraging and add important information to previous work in the field.
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