Abstract
This chapter presents a laboratory study of multi-class classification problem for multiple indoor air contaminants. The effectiveness of the proposed HSVM model has been rigorously evaluated. In addition, we have also compared with existing methods including Euclidean distance to centroids (EDC), simplified fuzzy ARTMAP network (SFAM), multilayer perceptron neural network (MLP) based on back-propagation, individual FLDA, and single SVM. Experimental results demonstrate that the HSVM model outperforms other classifiers in general. Also, HSVM classifier preliminarily shows its superiority in solution to discrimination in various electronic nose applications.
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Zhang, L., Tian, F., Zhang, D. (2018). Discriminative Support Vector Machine-Based Odor Classification. In: Electronic Nose: Algorithmic Challenges. Springer, Singapore. https://doi.org/10.1007/978-981-13-2167-2_6
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DOI: https://doi.org/10.1007/978-981-13-2167-2_6
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