Mutiple classifiers for electronic nose data

In this contribution we apply a method-called boosting-for constructing a classifier out of a set of (base or weak) classifiers for the discrimination of two groups of coffees (blends and monovarieties). The main idea of boosting is to produce a sequence of base classifiers that progressively concentrate on the hard patterns, i.e. those which are near to the classification boundary. Measurement were performed with the Pico-1 Electronic Nose based on thin, films semiconductor sensors developed in Brescia. The boosting algorithm was able to halve the classification error for the blends data and to diminish it from 21% to 18% for the more difficult monovarieties data set.