Chapter

Multiple Classifier Systems

Volume 2364 of the series Lecture Notes in Computer Science pp 262-271

Date:

Boosting and Classification of Electronic Nose Data

  • Francesco MasulliAffiliated withINFM, Istituto Nazionale per la Fisica della MateriaDipartimento di Informatica, Università di Pisa
  • , Matteo PardoAffiliated withINFM and Dipartimento di Chimica e Fisica
  • , Giorgio SberveglieriAffiliated withINFM and Dipartimento di Chimica e Fisica
  • , Giorgio ValentiniAffiliated withINFM, Istituto Nazionale per la Fisica della MateriaDISI - Dipartimento di Informatica e Scienze dell’Informazione, Università di Genova

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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.