A method for evaluating data-preprocessing techniques for odor classification with an array of gas sensors

The performance of a pattern recognition system is dependent on, among other things, an appropriate data-preprocessing technique. In this paper, we describe a method to evaluate the performance of a variety of these techniques for the problem of odor classification using an array of gas sensors, also referred to as an electronic nose. Four experimental odor databases with different complexities are used to score the data-preprocessing techniques. The performance measure used is the cross-validation estimate of the classification rate of a K nearest neighbor voting rule operating on Fisher's linear discriminant projection subspace.