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Enhancing WO3 gas sensor selectivity using a set of pollutant detection classifiers

Rabeb Faleh (Electrical Department, National School of Engineers of Sfax, Sfax, Tunisia)
Sami Gomri (Ecole Nationale d’Ingenieurs de Sfax, Sfax, Tunisia)
Mehdi Othman (Institut des Materiaux de Microelectronique et des Nanosciences de Provence, Marseille, France)
Khalifa Aguir (Institut des Materiaux de Microelectronique et des Nanosciences de Provence, Marseille, France)
Abdennaceur Kachouri (National School of Engineers of Sfax, Sfax, Tunisia)

Sensor Review

ISSN: 0260-2288

Article publication date: 5 December 2017

Issue publication date: 8 January 2018

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Abstract

Purpose

In this paper, a novel hybrid approach aimed at solving the problem of cross-selectivity of gases in electronic nose (E-nose) using the combination classifiers of support vector machine (SVM) and k-nearest neighbors (KNN) methods was proposed.

Design/methodology/approach

First, three WO3 sensors E-nose system was used for data acquisition to detect three gases, namely, ozone, ethanol and acetone. Then, two transient parameters, derivate and integral, were extracted for each gas response. Next, the principal component analysis (PCA) was been applied to extract the most relevant sensor data and dimensionality reduction. The new coordinates calculated by PCA were used as inputs for classification by the SVM method. Finally, the classification achieved by the KNN method was carried out to calculate only the support vectors (SVs), not all the data.

Findings

This work has proved that the proposed fusion method led to the highest classification rate (100 per cent) compared to the accuracy of the individual classifiers: KNN, SVM-linear, SVM-RBF, SVM-polynomial that present, respectively, 89, 75.2, 80 and 79.9 per cent as classification rate.

Originality/value

The authors propose a fusion classifier approach to improve the classification rate. In this method, the extracted features are projected into the PCA subspace to reduce the dimensionality. Then, the obtained principal components are introduced to the SVM classifier and calculated SVs which will be used in the KNN method.

Keywords

Citation

Faleh, R., Gomri, S., Othman, M., Aguir, K. and Kachouri, A. (2018), "Enhancing WO3 gas sensor selectivity using a set of pollutant detection classifiers", Sensor Review, Vol. 38 No. 1, pp. 65-73. https://doi.org/10.1108/SR-12-2016-0273

Publisher

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Emerald Publishing Limited

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