Limits...
Optimal Sensor Selection for Classifying a Set of Ginsengs Using Metal-Oxide Sensors.

Miao J, Zhang T, Wang Y, Li G - Sensors (Basel) (2015)

Bottom Line: The relation of the minimum numbers of sensors with number of samples in the sample set was revealed.The results showed that as the number of samples increased, the average minimum number of sensors increased, while the increment decreased gradually and the average optimal classification rate decreased gradually.Moreover, a new approach of sensor selection was proposed to estimate and compare the effective information capacity of each sensor.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, Zhejiang University, Hangzhou 310027, Zhejiang, China. jiacheng@zju.edu.cn.

ABSTRACT
The sensor selection problem was investigated for the application of classification of a set of ginsengs using a metal-oxide sensor-based homemade electronic nose with linear discriminant analysis. Samples (315) were measured for nine kinds of ginsengs using 12 sensors. We investigated the classification performances of combinations of 12 sensors for the overall discrimination of combinations of nine ginsengs. The minimum numbers of sensors for discriminating each sample set to obtain an optimal classification performance were defined. The relation of the minimum numbers of sensors with number of samples in the sample set was revealed. The results showed that as the number of samples increased, the average minimum number of sensors increased, while the increment decreased gradually and the average optimal classification rate decreased gradually. Moreover, a new approach of sensor selection was proposed to estimate and compare the effective information capacity of each sensor.

No MeSH data available.


Comparison of average classification accuracy of sensor sets with N (N = 1 to 12) sensors for LDA, SVM and KNN (k = 1, 3, 5, 7).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4541866&req=5

sensors-15-16027-f003: Comparison of average classification accuracy of sensor sets with N (N = 1 to 12) sensors for LDA, SVM and KNN (k = 1, 3, 5, 7).

Mentions: LDA, SVM and KNN (k = 1, 3, 5, 7) were employed and compared for discriminating sample set A1 = 1, 2, 3, 4, 5, 6, 7, 8, 9 (with all nine classes of sample) with all potential sensor sets. The average classification accuracy of sensor sets with N (N = 1 to 12) sensors were compared and shown in Figure 3. LDA and SVM-RBF achieved better classification accuracy than all KNNs except for LDA with N = 1. The performance of LDA and SVM-RBF is near, SVM-RBF performs better with N < 5 and LDA performs better with N > 5. Total time consumption of LDA and SVM-RBF is 1113 s and 47,599 s separately. SVM-RBF takes much more time than LDA. Thus, based on the assessment of classification accuracy and computational efficiency, LDA was chosen and employed in the following analysis, except for Section 3.5.


Optimal Sensor Selection for Classifying a Set of Ginsengs Using Metal-Oxide Sensors.

Miao J, Zhang T, Wang Y, Li G - Sensors (Basel) (2015)

Comparison of average classification accuracy of sensor sets with N (N = 1 to 12) sensors for LDA, SVM and KNN (k = 1, 3, 5, 7).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541866&req=5

sensors-15-16027-f003: Comparison of average classification accuracy of sensor sets with N (N = 1 to 12) sensors for LDA, SVM and KNN (k = 1, 3, 5, 7).
Mentions: LDA, SVM and KNN (k = 1, 3, 5, 7) were employed and compared for discriminating sample set A1 = 1, 2, 3, 4, 5, 6, 7, 8, 9 (with all nine classes of sample) with all potential sensor sets. The average classification accuracy of sensor sets with N (N = 1 to 12) sensors were compared and shown in Figure 3. LDA and SVM-RBF achieved better classification accuracy than all KNNs except for LDA with N = 1. The performance of LDA and SVM-RBF is near, SVM-RBF performs better with N < 5 and LDA performs better with N > 5. Total time consumption of LDA and SVM-RBF is 1113 s and 47,599 s separately. SVM-RBF takes much more time than LDA. Thus, based on the assessment of classification accuracy and computational efficiency, LDA was chosen and employed in the following analysis, except for Section 3.5.

Bottom Line: The relation of the minimum numbers of sensors with number of samples in the sample set was revealed.The results showed that as the number of samples increased, the average minimum number of sensors increased, while the increment decreased gradually and the average optimal classification rate decreased gradually.Moreover, a new approach of sensor selection was proposed to estimate and compare the effective information capacity of each sensor.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, Zhejiang University, Hangzhou 310027, Zhejiang, China. jiacheng@zju.edu.cn.

ABSTRACT
The sensor selection problem was investigated for the application of classification of a set of ginsengs using a metal-oxide sensor-based homemade electronic nose with linear discriminant analysis. Samples (315) were measured for nine kinds of ginsengs using 12 sensors. We investigated the classification performances of combinations of 12 sensors for the overall discrimination of combinations of nine ginsengs. The minimum numbers of sensors for discriminating each sample set to obtain an optimal classification performance were defined. The relation of the minimum numbers of sensors with number of samples in the sample set was revealed. The results showed that as the number of samples increased, the average minimum number of sensors increased, while the increment decreased gradually and the average optimal classification rate decreased gradually. Moreover, a new approach of sensor selection was proposed to estimate and compare the effective information capacity of each sensor.

No MeSH data available.