The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Multi-class AdaBoost
Open accessAbstract
Boosting has been a very successful technique for solving the two-class classification problem.In going from two-class to multi-class classification, most algorithms have been restricted to reducing the multi-class classification problem to multiple two-class problems.In this paper, we develop a new algorithm that directly extends the AdaBoost algorithm to the multi-class case without reducing it to multiple two-class problems.We show that the proposed multi-class AdaBoost algorithm is equivalent to a forward stagewise additive modeling algorithm that minimizes a novel exponential loss for multi-class classification.Furthermore, we show that the exponential loss is a member of a class of Fisher-consistent loss functions for multi-class classification.As shown in the paper, the new algorithm is extremely easy to implement and is highly competitive in terms of misclassification error rate.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Self-citationA decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
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Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
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Cited by
Dong, X., Yu, Z., Cao, W., Shi, Y., & Ma, Q. (2020) · Frontiers of Computer Science · doi:10.1007/s11704-019-8208-z · Cited by 1,928
Sun, H., Burton, H. V., & Huang, H. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2020.101816 · Cited by 544
Fake News Detection Using Machine Learning Ensemble Methods
Ahmad, I., Yousaf, M., Yousaf, S., & Ahmad, M. O. (2020) · Complexity · doi:10.1155/2020/8885861 · Cited by 474
Boosting methods for multi-class imbalanced data classification: an experimental review
Tanha, J., Abdi, Y., Samadi, N., Razzaghi, N., & Asadpour, M. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00349-y · Cited by 452
Towards explainable deep neural networks (xDNN)
Angelov, P., & Soares, E. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.07.010 · Cited by 292
Metadata
- Authors
- Hastie, T., Rosset, S., Zhu, J., & Zou, H.
- Venue
- Statistics and Its Interface
- Publisher
- International Press of Boston
- Published
- 2009
- Volume
- 2
- Issue
- 3
- Pages
- 349-360
- ISSN
- 1938-7989 (ISSN-L)
- 1938-7997
- OpenAlex
- W1968969471
- Cited by
- 1,875
- References
- 7
- FWCI
- 33.97
- Type
- journal article
- Language
- en
- Open access
- bronze
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