On combining classifiers using sum and product rules
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Among fusion strategies, the weighted arithmetic mean of per-model outputs is the least sensitive to errors of individual classifiers.
“A number of works have shown that among fusion strategies, the weighted arithmetic mean of per-model outputs is the least sensitive to errors of individual classifiers [50].”
From ModDrop: Adaptive Multi-Modal Gesture Recognition (2016) · cites this paper as [50]
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- Authors
- Alexandre, L. A., Campilho, A. C., & Kamel, M.
- Affiliations
- Universidade do Porto (PT)
- University of Waterloo (CA)
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2001-10
- Volume
- 22
- Issue
- 12
- Pages
- 1283-1289
- ISSN
- 0167-8655 (ISSN-L)
- OpenAlex
- W2029458478
- Cited by
- 117
- References
- 14
- Citations by year
- 2024: 1 · 2023: 4 · 2022: 4 · 2021: 5 · 2020: 3 · 2019: 2 · 2018: 2 · 2017: 3 · 2016: 4 · 2015: 8 · 2014: 5 · 2013: 6 · 2012: 10
- FWCI
- 2.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pattern Recognition Letters (publishedVersion)
- Funders
- Fundação para a Ciência e a Tecnologia
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- classifier combination, ensemble learning, sum rules, product rule, arithmetic mean, geometric mean, binary classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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