Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application
TL;DR: It is 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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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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References
Electronic nose: current status and future trends.
TL;DR: This research presents a meta-analysis of 126 existing and new technologies in the gas chromatography field, and some new technologies that are being developed, as well as suggestions for further studies.
How many principal components? stopping rules for determining the number of non-trivial axes revisited
TL;DR: A Bartlett's test is used to test the significance of the first principal component, indicating whether or not at least two variables share common variation in the entire data set, and a two-step approach appears to be highly effective.
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