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A0367
Title: The role of trimming and variable selection in robust model-based classification for food authenticity studies Authors:  Andrea Cappozzo - University of Milano Bicocca (Italy) [presenting]
Francesca Greselin - University of Milano Bicocca (Italy)
Thomas Brendan Murphy - University College Dublin (Ireland)
Abstract: Food authenticity studies deal with the detection of products that are not what they claim to be, thereby preventing economic fraud or possible damage to health. For identifying illegal sub-samples we introduce robustness in a semi-supervised model-based classification rule. That is, labelled and unlabelled data are jointly modeled by a Gaussian mixture model with parsimonious covariance structure. To avoid singularity issues, we adopt a restriction on the eigenvalues'ratio of the group scatter matrices. Adulterated observations are detected by monitoring their contributions to the overall observed likelihood, and following the impartial trimming established technique: the illegal sub-sample is the least plausible under the estimated model. A wrapper approach for variable selection is then considered, providing relevant information about discriminant variables and for feature reduction in a high-dimensional context. Experiments on real data, artificially adulterated, are provided to underline the benefits of the proposed method.