The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography
Abstract
Ensemble methodology, which builds a classification model by integrating multiple classifiers, can be used for improving prediction performance. Researchers from various disciplines such as statistics, pattern recognition, and machine learning have seriously explored the use of ensemble methodology. This paper presents an updated survey of ensemble methods in classification tasks, while introducing a new taxonomy for characterizing them. The new taxonomy, presented from the algorithm designer's point of view, is based on five dimensions: inducer, combiner, diversity, size, and members' dependency. We also propose several selection criteria, presented from the practitioner's point of view, for choosing the most suitable ensemble method.
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Cites
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Ensemble methods in machine learning
Dietterich, T. G. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45014-9_1 · Cited by 8,408
Wolpert, D. H. (1992) · Neural Networks · doi:10.1016/s0893-6080(05)80023-1 · Cited by 7,951
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
The Strength of Weak Learnability
Schapire, R. E. (1990) · Machine Learning · doi:10.1007/bf00116037 · Cited by 4,600
Hansen, L. K., & Salamon, P. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.58871 · Cited by 4,260
Popular Ensemble Methods: An Empirical Study
Opitz, D., & Maclin, R. (1999) · Journal of Artificial Intelligence Research · doi:10.1613/jair.614 · Cited by 2,953
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy
Kuncheva, L. I., & Whitaker, C. J. (2003) · Machine Learning · doi:10.1023/a:1022859003006 · Cited by 2,335
Cited by
Ensemble approaches for regression: A survey
Mendes-Moreira, J., Soares, C., Jorge, A. M., & Sousa, J. F. D. (2012) · ACM Computing Surveys · doi:10.1145/2379776.2379786 · Cited by 667
A Survey on Ensemble Learning for Data Stream Classification
Gomes, H. M., Barddal, J. P., Enembreck, F., & Bifet, A. (2018) · ACM Computing Surveys · doi:10.1145/3054925 · Cited by 579
A comparative study of classifier ensembles for bankruptcy prediction
Tsai, C.-F., Hsu, Y.-F., & Yen, D. C. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.08.047 · Cited by 202
DEEPSEC: A Uniform Platform for Security Analysis of Deep Learning Model
Ling, X., Ji, S., Zou, J., Wang, J., Wu, C., Li, B., & Wang, T. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00023 · Cited by 152
The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective
Franklin, G., Stephens, R., Piracha, M., Tiosano, S., Lehouillier, F., Koppel, R., & Elkin, P. (2024) · Life · doi:10.3390/life14060652 · Cited by 76
Metadata
- Authors
- Rokach, L.
- Venue
- Computational Statistics & Data Analysis
- Publisher
- Elsevier BV
- Published
- 2009-10
- Volume
- 53
- Issue
- 12
- Pages
- 4046-4072
- ISSN
- 0167-9473 (ISSN-L)
- OpenAlex
- W2004014581
- Cited by
- 253
- References
- 8
- FWCI
- 18.43
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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