Multi-Dimension Combining (MDC) in abstract level and Hierarchical MDC (HMDC) to improve the classification accuracy of enoses
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.
|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|
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).