Boosting and Classification of Electronic Nose Data

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Abstract

Boosting methods are known to improve generalization performances of learning algorithms reducing both bias and variance or enlarging the margin of the resulting multi-classifier system. In this contribution we applied Adaboost to the discrimination of different types of coffee using data produced with an Electronic Nose. Two groups of coffees (blends and monovarieties), consisting of seven classes each, have been analyzed. The boosted ensemble of Multi-Layer Perceptrons 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.