Ensemble Methods: Foundations and Algorithms
Abstract
An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field.After presenting background and terminology, the book cover
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The main advantage of ensemble methods is their ability to boost weak classifiers to strong classifiers.
“The main advantage of ensemble methods is their ability to boost the so called weak classifiers to strong classifiers.”
From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [39]
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- Authors
- Zhou, Z.-H.
- Corresponding author
- Zhou, Zhi-Hua
- ORCID
- Zhou, Zhi-Hua
- Venue
- Chapman & Hall/CRC eBooks
- Publisher
- Chapman and Hall/CRC
- Published
- 2012-06-06
- OpenAlex
- W4232478844
- Cited by
- 2,534
- References
- 0
- Citations by year
- 2026: 240 · 2025: 341 · 2024: 277 · 2023: 272 · 2022: 265 · 2021: 263 · 2020: 233 · 2019: 208 · 2018: 150 · 2017: 107 · 2016: 56 · 2015: 52 · 2014: 36 · 2013: 30 · 2012: 4
- FWCI
- 17.78
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Chapman & Hall/CRC eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Keywords
- ensemble methods, boosting, bagging, random forest, decision tree, machine learning algorithms, classification accuracy
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