Application of a Hybrid Classifier to the Recognition of Petrochemical Odors

Nowadays there are several data mining algorithms applied to the resolution of many different problems, such as the classification of patterns. However, when these algorithms are used separately to classify they usually present an inferior performance compared to the performance obtained by combined models. The bagging and boosting techniques combine models of the same kind in a competitive form, in other words, the output is generally provided by the winning classifier. Alternatively, stacking usually combines different algorithms, constituting a hybrid model. Nevertheless, stacking has a high cost, due to the search for the best models that will be combined to solve a certain problem. Thus, we present a hybrid classifier (HC) to be applied to the recognition of gases derived from petrol at a lower cost and in a cooperative way.