Transforming examples for multiclass boosting
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Summary
This work empirically applies the transformation on several multiclass datasets using naive Bayes and decision trees as base classifiers and shows that it is competitive with AdaBoost.ECC, a boosting algorithm using output coding.
- Type
- article
- Published
- 2010-03-01
- Cited by
- 0
- References
- 11
- OpenAlex
- https://openalex.org/W2083017474
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:7912235
Keywords
Boosting (machine learning), Computer science, AdaBoost, Artificial intelligence, Resampling
References
- C4.5: Programs for Machine Learning (書評)
- Using output codes to boost multiclass learning problems
- Multiclass learning, boosting, and error-correcting codes
- A decision-theoretic generalization of on-line learning and an application to boosting
- Improved Boosting Algorithms Using Confidence-rated Predictions
- Unifying the error-correcting and output-code AdaBoost within the margin framework
- Pattern classification and scene analysis
- Multiclass boosting with repartitioning
- C4.5: Programs for Machine Learning
- Experiments with a New Boosting Algorithm
- Experiments with a new boosting algorithm
- UCI Machine Learning Repository
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