The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities
Abstract
Abstract Ensembles, especially ensembles of decision trees, are one of the most popular and successful techniques in machine learning. Recently, the number of ensemble-based proposals has grown steadily. Therefore, it is necessary to identify which are the appropriate algorithms for a certain problem. In this paper, we aim to help practitioners to choose the best ensemble technique according to their problem characteristics and their workflow. To do so, we revise the most renowned bagging and boosting algorithms and their software tools. These ensembles are described in detail within their variants and improvements available in the literature. Their online-available software tools are reviewed attending to the implemented versions and features. They are categorized according to their supported programming languages and computing paradigms. The performance of 14 different bagging and boosting based ensembles, including XGBoost, LightGBM and Random Forest, is empirically analyzed in terms of predictive capability and efficiency. This comparison is done under the same software environment with 76 different classification tasks. Their predictive capabilities are evaluated with a wide variety of scenarios, such as standard multi-class problems, scenarios with categorical features and big size data. The efficiency of these methods is analyzed with considerably large data-sets. Several practical perspectives and opportunities are also exposed for ensemble learning.
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Cited by
Nakhle, F., & Harfouche, A. L. (2021) · Patterns · doi:10.1016/j.patter.2021.100323 · Cited by 28
Enhancing Debris Flow Warning via Machine Learning Feature Reduction and Model Selection
Zhou, Q., Tang, H., Hibert, C., Chmiel, M., Walter, F., Dietze, M., & Turowski, J. M. (2025) · Journal of Geophysical Research: Earth Surface · doi:10.1029/2024jf008094 · Cited by 20
Bringing Machine Learning Classifiers Into Critical Cyber-Physical Systems: A Matter of Design
Sayin, B., Zoppi, T., Marchini, N., Khokhar, F. A., & Passerini, A. (2025) · IEEE Access · doi:10.1109/access.2025.3568501 · Cited by 7
“…three statistical classifiers (Gaussian Naïve Bayes GNB, linear discriminant analysis LDA, logistic regression LR); and four tree-based classifiers that were recently benchmarked in [21]: decision trees DT, random forests RF, extremely randomized trees ET, and eXtreme Gradient Boosting XGB.”
Metadata
- Authors
- González, S., García, S., Del Ser, J., Rokach, L., & Herrera, F.
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 64
- Pages
- 205-237
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W3044867970
- Cited by
- 582
- References
- 19
- FWCI
- 22.26
- Type
- journal article
- Language
- en
- Open access
- closed
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