Decompositive classification models for electronic noses

The classification of 242 measurements in 14 classes is attempted using two different classification approaches. Measurements have been performed with a commercial electronic nose (EN) comprising 11 chemical sensors on extra-virgin olive oils of 14 different geographical provenances. As we deal with a relatively small data set and a big number of classes, the classification task is quite demanding. We first tackled the global classification task using a single multilayer perceptron (MLP), which gave a misclassification rate of 25%. In order to improve the performance, we studied two different approaches based on ensembles of learning machines, which decompose the classification in subtasks. In the first approach, a classification tree was constructed using a priori knowledge (geographical origin) for the formation of sensible superclasses (union of single classes). At each classification node we both used MLPs and SIMCA (soft independent modeling of class analogy). The second approach applies a learning machine called parallel nonlinear dichotomizers (PND) that is based on the decomposition of a K-class classification problem in a set of two-class tasks. A binary codeword is assigned to each class and each bit is learned by a dichotomizer (implemented by a dedicated MLP). In the reconstruction stage, a pattern is assigned to the class whose codeword is most similar (e.g. in L1 norm) to the output of the set of dichotomizers. We achieved the best results (misclassification error rate of about 10%) using a decomposition based on error correcting output codes (ECOC).