Probabilistic support vector machines for multi-class alcohol identification

In this work we address the use of support vector machines in multi-category problems. In our case, the objective is to classify eight different kinds of alcohols with just one SnO2 sensor using thermomodulation. The use of support vector machines in the field of sensors signals recognition is beginning to be used due to the ability to generalize in a binary classification problem with a small number of training samples. However, when a multi-class problem is presented, the outputs of the support vector machines are uncalibrated and should not be used to determine the category. In this work a step forward is added to the output of the binary classifiers to choose the category with a maximal a posteriori probability. Obtained results show that the ability of generalization provided by support vector machines improves the results obtained with other learning methods used in the electronic nose field and their use in multi-class problems can be addressed with the method proposed. To reduce the high dimensionality of the data we have benchmarked several feature extraction methods with probabilistic support vector machines.