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Convex Hull Ensemble Machine for Regression and Classification
- Title
- Convex Hull Ensemble Machine for Regression and Classification
- Authors
- Kim, Yongdai; Kim, Jinseog
- Ewha Authors
- 김용대
- Issue Date
- 2004
- Journal Title
- KNOWLEDGE AND INFORMATION SYSTEMS
- ISSN
- 0219-1377
- Citation
- KNOWLEDGE AND INFORMATION SYSTEMS vol. 6, no. 6, pp. 645 - 663
- Keywords
- Bagging; Boosting; Classification; Ensemble; Regression
- Publisher
- SPRINGER LONDON LTD
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- We propose a new ensemble algorithm called Convex Hull Ensemble Machine (CHEM). CHEM in Hilbert space is first developed and modified for regression and classification problems. We prove that the ensemble model converges to the optimal model in Hilbert space under regularity conditions. Empirical studies reveal that, for classification problems, CHEM has a prediction accuracy similar to that of boosting, but CHEM is much more robust with respect to output noise and never overfits datasets even when boosting does. For regression problems, CHEM is competitive with other ensemble methods such as gradient boosting and bagging.
- DOI
- 10.1007/s10115-003-0116-7
- Appears in Collections:
- 자연과학대학 > 통계학전공 > Journal papers
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