Classification of data from electronic nose using relevance vector machines

Abstract

An approach to classify the data from an electronic nose is investigated in this paper. This approach is based on the method of the relevance vector machines (RVM). The electronic nose data are first converted into principal components using the principal component analysis (PCA) method and then directly sent as inputs to a RVM classifier. The performance of the developed approach is validated by cross-validation procedure. Some experiments have been performed using different combinations of original coffee data, including the test data from a multi-class classification problem as well as the data from some two-class classification problems having different kinds of hardness. Experimental results show that the RVM method is an effective technique for the classification of electronic nose data. Compared with the support vector machine (SVM) method, the RVM method can provide similar classification accuracy with dramatically fewer kernel functions. In addition, another advantage of RVM method is its fewer parameter settings, in which case only one kernel parameter is needed.