Cited by 117Machine Learning and Data ClassificationImbalanced Data Classification TechniquesData Mining and Machine Learning Applications

On combining classifiers using sum and product rules

Alexandre, Luís A., Campilho, Aurélio C., Kamel, Mohamed·2001·Pattern Recognition Letters

Integrity signals

Registry

Crossref · 10.1016/s0167-8655(01)00073-3

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14 of 14 references verified0 citation errors · 0 not checkable

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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

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