Multi-Dimension Combining (MDC) in abstract Level and Hierarchical MDC (HMDC) to Improve the Classification Accuracy of Enoses

Author

Chen, Hong ; Goubran, Rafik A. ; Mussivand, Tofy

Author_Institution

Dept. of Syst. & Comput. Eng., Carleton Univ., Ottawa, Ont.

Volume

1

fYear

2005

fDate

16-19 May 2005

Firstpage

683

Lastpage

686

Abstract

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 multilayer 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 multidimension combining). The performance of the proposed algorithm is evaluated using experimental data and Enose device of Cyranose 320

Keywords

electronic noses; feature extraction; multilayer perceptrons; pattern classification; Cyranose 320; PARC method; classification algorithm; electronic noses; feature extraction; hierarchical MDC; k-nearest neighbor; linear discriminant analysis; multi-dimension combining; multilayer perceptron; pattern recognition; probabilistic neural network; Electronic noses; Feature extraction; Linear discriminant analysis; Multi-layer neural network; Multilayer perceptrons; Neural networks; Pattern recognition; Sensor arrays; Signal processing algorithms; Systems engineering and theory; HMDC (Hierarchical MDC); MDC (Multi-Dimension Combining); dimension reduction; electronic nose; feature extraction; pattern recognition (PARC);

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference, 2005. IMTC 2005. Proceedings of the IEEE

Conference_Location

Ottawa, Ont.

Print_ISBN

0-7803-8879-8

Type

conf

DOI

10.1109/IMTC.2005.1604204

Filename

1604204