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Convex Hull Ensemble Machine for Regression and Classification

Title
Convex Hull Ensemble Machine for Regression and Classification
Authors
Kim, YongdaiKim, Jinseog
Ewha Authors
김용대
Issue Date
2004
Journal Title
KNOWLEDGE AND INFORMATION SYSTEMS
ISSN
0219-1377JCR Link
Citation
vol. 6, no. 6, pp. 645 - 663
Keywords
BaggingBoostingClassificationEnsembleRegression
Publisher
SPRINGER LONDON LTD
Indexed
SCIE; SCOPUS WOS
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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