Semantic Scholar Open Access 2019 1366 sitasi

Ensemble Methods

Duen Horng Chau Georgia Tech Mahdi Roozbahani Jeffrey Heer +2 lainnya

Abstrak

The idea of ensemble learning is to employ multiple learners and combine their predictions. There is no definitive taxonomy. Jain, Duin and Mao (2000) list eighteen classifier combination schemes; Witten and Frank (2000) detail four methods of combining multiple models: bagging, boosting, stacking and errorcorrecting output codes whilst Alpaydin (2004) covers seven methods of combining multiple learners: voting, error-correcting output codes, bagging, boosting, mixtures of experts, stacked generalization and cascading. We focus on four methods, then review the literature in general, with, where possible, an emphasis on both theory and practical advice.

Penulis (7)

Duen Horng

Chau

Georgia Tech

Mahdi Roozbahani

Jeffrey Heer

J. Stasko

C. Faloutsos

Format Sitasi

APA MLA BibTeX

Horng, D., Chau, Tech, G., Roozbahani, M., Heer, J., Stasko, J. et al. (2019). Ensemble Methods. https://doi.org/10.1017/9781139236003.012

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Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 1366×
Sumber Database: Semantic Scholar
DOI: 10.1017/9781139236003.012
Akses: Open Access ✓