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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.

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The 3D-PCA plot of three classes of wine derived from three different cultivars.Note: Symbols: O-Class 1; *-Class 2 and □-Class 3.
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f5-sensors-12-02818: The 3D-PCA plot of three classes of wine derived from three different cultivars.Note: Symbols: O-Class 1; *-Class 2 and □-Class 3.

Mentions: In this paper, the PCA technique is used for feature selection. The aim is to pick out patterns in multivariate data and reduce the dimensionality of the input vector without a significant loss of information. PCA can also help to get an overall view of these data through giving an appropriate visual representation with fewer dimensions. Figure 5 shows the 3D-PCA plot of three classes of wine. Examining the PCA plot, the first three principal components (PCs) account for 66.5% of the variance of the data. There is not a good clustering of three classes in 3D plot. Some samples of one class drift into feature spaces of other classes. It implies that the first three principal components do not represent sufficient information for the data set. The some other of principal components should be put in to preserve more of the relevant information of the original data. There are some guidelines for determining the optimal amount of PCs [31,32]. In the following part of this section, more than three principal components will present to the KIII model as feature vector. And the correct classification rates are calculated to make sure how many principal components are sufficient.


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 3D-PCA plot of three classes of wine derived from three different cultivars.Note: Symbols: O-Class 1; *-Class 2 and □-Class 3.
© Copyright Policy
Related In: Results  -  Collection

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

f5-sensors-12-02818: The 3D-PCA plot of three classes of wine derived from three different cultivars.Note: Symbols: O-Class 1; *-Class 2 and □-Class 3.
Mentions: In this paper, the PCA technique is used for feature selection. The aim is to pick out patterns in multivariate data and reduce the dimensionality of the input vector without a significant loss of information. PCA can also help to get an overall view of these data through giving an appropriate visual representation with fewer dimensions. Figure 5 shows the 3D-PCA plot of three classes of wine. Examining the PCA plot, the first three principal components (PCs) account for 66.5% of the variance of the data. There is not a good clustering of three classes in 3D plot. Some samples of one class drift into feature spaces of other classes. It implies that the first three principal components do not represent sufficient information for the data set. The some other of principal components should be put in to preserve more of the relevant information of the original data. There are some guidelines for determining the optimal amount of PCs [31,32]. In the following part of this section, more than three principal components will present to the KIII model as feature vector. And the correct classification rates are calculated to make sure how many principal components are sufficient.

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