ArticleSensors (Basel, Switzerland)2015
Metal oxide gas sensor drift compensation using a two-dimensional classifier ensemble.
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 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Autonomous Hazardous Gas Detection Systems: A Systematic Review.Sensors (Basel, Switzerland) · 2025Review
- Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods.Sensors (Basel, Switzerland) · 2021Review
- Miniaturised Infrared Spectrophotometer for Low Power Consumption Multi-Gas Sensing.Sensors (Basel, Switzerland) · 2020Article
- A Suppression Method of Concentration Background Noise by Transductive Transfer Learning for a Metal Oxide Semiconductor-Based Electronic Nose.Sensors (Basel, Switzerland) · 2020Article
- Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System.Sensors (Basel, Switzerland) · 2019Article
- Gas-Sensor Drift Counteraction with Adaptive Active Learning for an Electronic Nose.Sensors (Basel, Switzerland) · 2018Article
- Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sensor drift is the most challenging problem in gas sensing at present. We propose a novel two-dimensional classifier ensemble strategy to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. This strategy is appropriate for multi-class classifiers that consist of combinations of pairwise classifiers, such as support vector machines. We compare the performance of the strategy with those of competing methods in an experiment based on a public dataset that was compiled over a period of three years. The experimental results demonstrate that the two-dimensional ensemble outperforms the other methods considered. Furthermore, we propose a pre-aging process inspired by that applied to the sensors to improve the stability of the classifier ensemble. The experimental results demonstrate that the weight of each multi-class classifier model in the ensemble remains fairly static before and after the addition of new classifier models to the ensemble, when a pre-aging procedure is applied.
Identifiers
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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.