ArticleSensors (Basel, Switzerland)2015
Optimal Sensor Selection for Classifying a Set of Ginsengs Using Metal-Oxide Sensors.
Article in Sensors (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Development of a Low-Cost Electronic Nose for Detection of Pathogenic Fungi and Applying It toSensors (Basel, Switzerland) · 2021Article
- Odor Detection Using an E-Nose With a Reduced Sensor Array.Sensors (Basel, Switzerland) · 2020Article
- Discrimination of Different Species of Dendrobium with an Electronic Nose Using Aggregated Conformal Predictor.Sensors (Basel, Switzerland) · 2019Article
- Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework.Sensors (Basel, Switzerland) · 2018Article
- Conformal Prediction Based on K-Nearest Neighbors for Discrimination of Ginsengs by a Home-Made Electronic Nose.Sensors (Basel, Switzerland) · 2017Article
- Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose.Sensors (Basel, Switzerland) · 2016Article
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4 authors.
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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.
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