E-Nose Vapor Identification Based on Dempster-Shafer Fusion of Multiple Classifiers

Electronic noses (e-noses) are commonly used to monitor air contaminants in space stations and shuttles. Data preprocessing (measurement denoising and feature extraction) and pattern classification are important problems of an e-nose system. In this paper, the application of a wavelet-based denoising method and a Dempster-Shafer (DS) classification fusion method in an e-nose system are proposed. Six transient-state features are extracted from the sensor measurements filtered by the wavelet denoising method and are used to train multiple classifiers such as multilayer perceptrons (MLPs), support vector machines (SVMs), k -nearest neighbors (KNNs), and the Parzen classifier. The DS technique is used at the end to fuse the results of the multiple classifiers to get the final classification. Experimental analysis based on real vapor data shows that the wavelet denoising method can successfully remove both random noise and outliers, and the classification rate can be improved by using classifier fusion.