ArticleSensors (Basel, Switzerland)2012
Pattern classification using an olfactory model with PCA feature selection in electronic noses: study and application.
Article in Sensors (Basel, Switzerland), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Electronic Nose-Based Exhaled Volatile Organic Compound Pattern Recognition and Multivariate Signal Analysis for Discriminating Idiopathic Pulmonary Fibrosis from Autoimmune Usual Interstitial Pneumonia.Sensors (Basel, Switzerland) · 2026Article
- Rapid Method of Wastewater Classification by Electronic Nose for Performance Evaluation of Bioreactors with Activated Sludge.Sensors (Basel, Switzerland) · 2023Article
- An improved AdaBoost algorithm for identification of lung cancer based on electronic nose.Heliyon · 2023Article
- Automated Channel Selection in High-Density sEMG for Improved Force Estimation.Sensors (Basel, Switzerland) · 2020Article
- Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples.Sensors (Basel, Switzerland) · 2018Article
- Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose.Sensors (Basel, Switzerland) · 2017Article
- Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose.Sensors (Basel, Switzerland) · 2016Article
- Optimal Sensor Selection for Classifying a Set of Ginsengs Using Metal-Oxide Sensors.Sensors (Basel, Switzerland) · 2015Article
- A novel low-cost sensor prototype for monitoring temperature during wine fermentation in tanks.Sensors (Basel, Switzerland) · 2013Article
- Diverse applications of electronic-nose technologies in agriculture and forestry.Sensors (Basel, Switzerland) · 2013Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.