A general framework for evaluating real-time bioaerosol classification algorithms Article Swipe
Marie-Pierre Meurville
,
Bernard Clot
,
Sophie Erb
,
Mária Lbadaoui-Darvas
,
Fiona Tummon
,
Gian-Duri Lieberherr
,
Benoît Crouzy
,
Marie-Pierre Meurville
,
Bernard Clot
,
Sophie Erb
,
Mária Lbadaoui-Darvas
,
Fiona Tummon
,
Gian-Duri Lieberherr
,
Benoît Crouzy
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-2025-5440
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-2025-5440
Advances in automatic bioaerosol monitoring require updated approaches to evaluate particle classification algorithms. We present a training and evaluation framework based on three metrics: (1) Kendall’s Tau correlation between predicted and manual concentrations, (2) scaling factor, to assess identification efficiency, and (3) off-season noise ratio, quantifying off-season false predictions. Metrics are computed per class across confidence thresholds and five stations stations, and visualised in graphs revealing overfitting, station-specific biases, and sensitivity–specificity trade-offs. We provide optimal ranges for each metric respectively calculated from correlations on co-located manual measurements, worst-case scenario off-season noise ratio, and physical sampling limits constraining acceptable scaling factor. The evaluation framework was applied to seven deep-learning classifiers trained on holography and fluorescence data from SwisensPoleno devices, and compared with the 2022 holography-only classifier. Classifier performances are compared through visualisation methods, helping identifying over-training, misclassification between morphologically similar taxa or between pollen and non-pollen particles. This methodology allows a transparent and reproducible comparison of classification algorithms, independent of classifier architecture and device. Its adoption could help standardise performance reporting across the research community, even more so when evaluation datasets are standardised across different regions.
Related Topics
Concepts
Classifier (UML)
Computer science
Artificial intelligence
Data mining
Machine learning
Pattern recognition (psychology)
A priori and a posteriori
Statistical classification
Boosting (machine learning)
Scaling
Visualization
Noise (video)
Metric (unit)
Random forest
Training set
Resampling
Multidimensional scaling
Contextual image classification
Background noise
Ensemble learning
Identification (biology)
Robustness (evolution)
Support vector machine
Algorithm
Sampling (signal processing)
Confusion matrix
Data visualization
Scale (ratio)
Metadata
- Type
- article
- Landing Page
- https://doi.org/10.5194/egusphere-2025-5440
- https://egusphere.copernicus.org/preprints/2025/egusphere-2025-5440/egusphere-2025-5440.pdf
- OA Status
- gold
- OpenAlex ID
- https://openalex.org/W7106006027
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W7106006027Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/egusphere-2025-5440Digital Object Identifier
- Title
-
A general framework for evaluating real-time bioaerosol classification algorithmsWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-19Full publication date if available
- Authors
-
Marie-Pierre Meurville, Bernard Clot, Sophie Erb, Mária Lbadaoui-Darvas, Fiona Tummon, Gian-Duri Lieberherr, Benoît Crouzy, Marie-Pierre Meurville, Bernard Clot, Sophie Erb, Mária Lbadaoui-Darvas, Fiona Tummon, Gian-Duri Lieberherr, Benoît CrouzyList of authors in order
- Landing page
-
https://doi.org/10.5194/egusphere-2025-5440Publisher landing page
- PDF URL
-
https://egusphere.copernicus.org/preprints/2025/egusphere-2025-5440/egusphere-2025-5440.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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-
https://egusphere.copernicus.org/preprints/2025/egusphere-2025-5440/egusphere-2025-5440.pdfDirect OA link when available
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Classifier (UML), Computer science, Artificial intelligence, Data mining, Machine learning, Pattern recognition (psychology), A priori and a posteriori, Statistical classification, Boosting (machine learning), Scaling, Visualization, Noise (video), Metric (unit), Random forest, Training set, Resampling, Multidimensional scaling, Contextual image classification, Background noise, Ensemble learning, Identification (biology), Robustness (evolution), Support vector machine, Algorithm, Sampling (signal processing), Confusion matrix, Data visualization, Scale (ratio)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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