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On combining classifiers using sum and product rules
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“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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Metadata
- 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
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