Abstract
The pattern recognition problem for real-life applications of gas identification is challenging due to the limited amount of data existing and the sequential variability of the mechanism mostly caused by drift and the real-time detection. These problems are commonly caused by the slow response of most of the gas sensors. In this paper, a novel gas identification approach based on the cluster-k-nearest neighbor (C-k-NN) is introduced. The effectiveness of this approach has been successfully demonstrated on the experimental data set obtained from array of gas sensors. Our classification takes advantages of both the k-NN, which is highly accurate, and the k-means cluster, which is able to reduce the classification time. In order to increase the accuracy rate, a new feature selection method is proposed. The selection of features is based on their ability to separate and distinguish between different classes. Advanced statistical metrics are introduced to quantify the classification contribution of each feature. Mostly, classifiers are suffering from misclassification detection; new statistical metrics are introduced to estimate the exactness of the classifier response, i.e., to detect the misclassification. To enhance the classification performances for gas identification, a new tree classification design is introduced, named tree C-k-NN. In order to assess the technique, experiments were conducted on six different gases. Accuracy rate of 98.7% has been obtained with the C-k-NN and 100% with the tree C-k-NN. The performance of this approach is also validated using three publicly available data sets.
| Original language | English |
|---|---|
| Article number | 6934986 |
| Pages (from-to) | 1705-1715 |
| Number of pages | 11 |
| Journal | IEEE Sensors Journal |
| Volume | 15 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1 Mar 2015 |
| Externally published | Yes |
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Keywords
- classification
- Clustering
- Confidence coefficient
- feature selection
- gas identification
- Gaussian Mixture Model (GMM)
- K-Means
ASJC Scopus subject areas
- Instrumentation
- Electrical and Electronic Engineering
Cite this
Advanced statistical metrics for gas identification system with quantification feedback. / Brahim-Belhaouari, Samir; Hassan, Muhammad; Walter, Nicolas; Bermak, Amine.
In: IEEE Sensors Journal, Vol. 15, No. 3, 6934986, 01.03.2015, p. 1705-1715.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Advanced statistical metrics for gas identification system with quantification feedback
AU - Brahim-Belhaouari, Samir
AU - Hassan, Muhammad
AU - Walter, Nicolas
AU - Bermak, Amine
PY - 2015/3/1
Y1 - 2015/3/1
N2 - The pattern recognition problem for real-life applications of gas identification is challenging due to the limited amount of data existing and the sequential variability of the mechanism mostly caused by drift and the real-time detection. These problems are commonly caused by the slow response of most of the gas sensors. In this paper, a novel gas identification approach based on the cluster-k-nearest neighbor (C-k-NN) is introduced. The effectiveness of this approach has been successfully demonstrated on the experimental data set obtained from array of gas sensors. Our classification takes advantages of both the k-NN, which is highly accurate, and the k-means cluster, which is able to reduce the classification time. In order to increase the accuracy rate, a new feature selection method is proposed. The selection of features is based on their ability to separate and distinguish between different classes. Advanced statistical metrics are introduced to quantify the classification contribution of each feature. Mostly, classifiers are suffering from misclassification detection; new statistical metrics are introduced to estimate the exactness of the classifier response, i.e., to detect the misclassification. To enhance the classification performances for gas identification, a new tree classification design is introduced, named tree C-k-NN. In order to assess the technique, experiments were conducted on six different gases. Accuracy rate of 98.7% has been obtained with the C-k-NN and 100% with the tree C-k-NN. The performance of this approach is also validated using three publicly available data sets.
AB - The pattern recognition problem for real-life applications of gas identification is challenging due to the limited amount of data existing and the sequential variability of the mechanism mostly caused by drift and the real-time detection. These problems are commonly caused by the slow response of most of the gas sensors. In this paper, a novel gas identification approach based on the cluster-k-nearest neighbor (C-k-NN) is introduced. The effectiveness of this approach has been successfully demonstrated on the experimental data set obtained from array of gas sensors. Our classification takes advantages of both the k-NN, which is highly accurate, and the k-means cluster, which is able to reduce the classification time. In order to increase the accuracy rate, a new feature selection method is proposed. The selection of features is based on their ability to separate and distinguish between different classes. Advanced statistical metrics are introduced to quantify the classification contribution of each feature. Mostly, classifiers are suffering from misclassification detection; new statistical metrics are introduced to estimate the exactness of the classifier response, i.e., to detect the misclassification. To enhance the classification performances for gas identification, a new tree classification design is introduced, named tree C-k-NN. In order to assess the technique, experiments were conducted on six different gases. Accuracy rate of 98.7% has been obtained with the C-k-NN and 100% with the tree C-k-NN. The performance of this approach is also validated using three publicly available data sets.
KW - classification
KW - Clustering
KW - Confidence coefficient
KW - feature selection
KW - gas identification
KW - Gaussian Mixture Model (GMM)
KW - K-Means
UR - http://www.scopus.com/inward/record.url?scp=84921059027&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84921059027&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2014.2364687
DO - 10.1109/JSEN.2014.2364687
M3 - Article
VL - 15
SP - 1705
EP - 1715
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
SN - 1530-437X
IS - 3
M1 - 6934986
ER -