This paper proposes a new classification algorithm for improving the accuracy of Electronic Noses. The algorithm extends the conventional Multi-Dimension Combining (MDC) of measurement level (PARC method as Multi-layer perceptron, or MLP ) into abstract level (PARC methods as K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA) and Probabilistic Neural Network (PNN)) and Hierarchical level (HMDC, or Hierarchical Multi-Dimension Combining). The performance of the proposed algorithm is evaluated using experimental data and Enose device of Cyranose 320.

Additional Metadata
Keywords Dimension reduction, Electronic nose, Feature extraction, HMDC (hierarchical MDC), MDC (multi-dimension combining), Pattern recognition (PARC)
Conference IMTC'05 - Proceedings of the IEEE Instrumentation and Measurement Technology Conference
Citation
Hong, C. (Chen), Goubran, R, & Mussivand, T. (Tofy). (2005). Multi-Dimension Combining (MDC) in abstract level and Hierarchical MDC (HMDC) to improve the classification accuracy of enoses. In Conference Record - IEEE Instrumentation and Measurement Technology Conference (pp. 683–686).