Limits...
Pattern classification using an olfactory model with PCA feature selection in electronic noses: study and application.

Fu J, Huang C, Xing J, Zheng J - Sensors (Basel) (2012)

Bottom Line: The principal component analysis technique was applied for feature selection and dimension reduction.In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector.We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science & Information Engineering, Zhejiang Gongshang University, Hangzhou, China. junfu@zjgsu.edu.cn

ABSTRACT
Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

Show MeSH

Related in: MedlinePlus

The 2D-PCA plot of five classes of green tea derived from five different provinces of China.Note: Symbols: O-class 1; *-class 2; □-class 3; ♦-class 4; x-class 5.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3376605&req=5

f8-sensors-12-02818: The 2D-PCA plot of five classes of green tea derived from five different provinces of China.Note: Symbols: O-class 1; *-class 2; □-class 3; ♦-class 4; x-class 5.

Mentions: Five brands of commercial green tea derived from five different provinces of China, i.e., Anhui, Henan, Hubei, Sichuan and Zhejiang (labeled as Class 1 to Class 5 in turn), represent different cultivars and tea-making process technology. For each brands of green tea, 22 measurements were made through the customized e-nose instrumentation described in Section 2.3. Every measurement lasted more than 400 sec with a sampling rate of 20 Sa/sec, and yielded a large data matrix. PCA was also used here to investigate how the response vectors from sensor arrays cluster and reduce the dimensionality of the raw data. Figure 8 shows the 2D-PCA plot of five classes of green tea. Examining the PCA plot, the first two principal components account for 97.26% of the variance of the data-set (PC1 and PC2 accounted for 55.83% and 41.43% of the variance, whereas PC3 and PC4 accounted for 1.96% and 0.53%, respectively). Despite the slight overlap of two clusters, we can easily observe an excellent discrimination of the five kinds of green tea.


Pattern classification using an olfactory model with PCA feature selection in electronic noses: study and application.

Fu J, Huang C, Xing J, Zheng J - Sensors (Basel) (2012)

The 2D-PCA plot of five classes of green tea derived from five different provinces of China.Note: Symbols: O-class 1; *-class 2; □-class 3; ♦-class 4; x-class 5.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376605&req=5

f8-sensors-12-02818: The 2D-PCA plot of five classes of green tea derived from five different provinces of China.Note: Symbols: O-class 1; *-class 2; □-class 3; ♦-class 4; x-class 5.
Mentions: Five brands of commercial green tea derived from five different provinces of China, i.e., Anhui, Henan, Hubei, Sichuan and Zhejiang (labeled as Class 1 to Class 5 in turn), represent different cultivars and tea-making process technology. For each brands of green tea, 22 measurements were made through the customized e-nose instrumentation described in Section 2.3. Every measurement lasted more than 400 sec with a sampling rate of 20 Sa/sec, and yielded a large data matrix. PCA was also used here to investigate how the response vectors from sensor arrays cluster and reduce the dimensionality of the raw data. Figure 8 shows the 2D-PCA plot of five classes of green tea. Examining the PCA plot, the first two principal components account for 97.26% of the variance of the data-set (PC1 and PC2 accounted for 55.83% and 41.43% of the variance, whereas PC3 and PC4 accounted for 1.96% and 0.53%, respectively). Despite the slight overlap of two clusters, we can easily observe an excellent discrimination of the five kinds of green tea.

Bottom Line: The principal component analysis technique was applied for feature selection and dimension reduction.In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector.We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science & Information Engineering, Zhejiang Gongshang University, Hangzhou, China. junfu@zjgsu.edu.cn

ABSTRACT
Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

Show MeSH
Related in: MedlinePlus