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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 topological structure of the KIII olfactory model [19].Notes: The lateral connection weights of M1 nodes (red arrows) are adjustable for learning, while the others (black arrows) are usually fixed for system stability.
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f1-sensors-12-02818: The topological structure of the KIII olfactory model [19].Notes: The lateral connection weights of M1 nodes (red arrows) are adjustable for learning, while the others (black arrows) are usually fixed for system stability.

Mentions: Over the last decades of study on mammalian olfactory system, Freeman and his colleagues [21–23] developed an olfactory model entitled KIII, which appropriately describes the whole olfactory pathway from sensory neuron to olfactory cortex, as well as offering a probable interpretation of principle of biological olfaction in the concept of mesoscopic neurodynamics [24]. The KIII model is a massively parallel architecture with multiple layers coupled with both feed-forward and feedback loops through distributed delay lines, as shown in Figure 1. External stimulus signals from receptors (R) transmit to periglomerular cells (P) and then to the olfactory bulb (OB) layer via the primary olfactory nerve (PON) in parallel. The OB layer consists of a set of mutually coupled neural oscillators, each being formed by two granule cells (G) and two mitral cells (M). Then the total output of all M1 nodes transmits via a lateral olfactory tract (LOT) to the anterior olfactory nucleus (AON) and prepyriform cortex (PC) layer, which provides the final output of the system to other parts of the brain from deep pyramidal cells (C), meanwhile back to the OB and AON layers [8].


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 topological structure of the KIII olfactory model [19].Notes: The lateral connection weights of M1 nodes (red arrows) are adjustable for learning, while the others (black arrows) are usually fixed for system stability.
© Copyright Policy
Related In: Results  -  Collection

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

f1-sensors-12-02818: The topological structure of the KIII olfactory model [19].Notes: The lateral connection weights of M1 nodes (red arrows) are adjustable for learning, while the others (black arrows) are usually fixed for system stability.
Mentions: Over the last decades of study on mammalian olfactory system, Freeman and his colleagues [21–23] developed an olfactory model entitled KIII, which appropriately describes the whole olfactory pathway from sensory neuron to olfactory cortex, as well as offering a probable interpretation of principle of biological olfaction in the concept of mesoscopic neurodynamics [24]. The KIII model is a massively parallel architecture with multiple layers coupled with both feed-forward and feedback loops through distributed delay lines, as shown in Figure 1. External stimulus signals from receptors (R) transmit to periglomerular cells (P) and then to the olfactory bulb (OB) layer via the primary olfactory nerve (PON) in parallel. The OB layer consists of a set of mutually coupled neural oscillators, each being formed by two granule cells (G) and two mitral cells (M). Then the total output of all M1 nodes transmits via a lateral olfactory tract (LOT) to the anterior olfactory nucleus (AON) and prepyriform cortex (PC) layer, which provides the final output of the system to other parts of the brain from deep pyramidal cells (C), meanwhile back to the OB and AON layers [8].

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