Synonyms
Classifier combination; Committee-based learning; Multiple classifier systems
Definition
Ensemble learning is a machine learning paradigm where multiple learners are trained to solve the same problem. In contrast to ordinary machine learning approaches which try to learn one hypothesis from training data, ensemble methods try to construct a set of hypotheses and combine them to use.
Introduction
An ensemble contains a number of learners which are usually called base learners. The generalization ability of an ensemble is usually much stronger than that of base learners. Actually, ensemble learning is appealing because it is able to boost weak learners which are slightly better than random guess to strong learnerswhich can make very accurate predictions. So, “base learners” are also referred to as “weak learners.” It is noteworthy, however, that although most theoretical analyses work on weak learners, base learners used in practice are not necessarily weak since using...
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
L.K. Hansen, P. Salamon, Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993–1001 (1990)
R.E. Schapire, The strength of weak learnability. Mach. Learn. 5(2), 197–227 (1990)
A. Krogh, J. Vedelsby, Neural network ensembles, cross validation, and active learning, in Advances in Neural Information Processing Systems, ed. by G. Tesauro, D.S. Touretzky, T.K. Leen, vol. 7 (MIT, Cambridge, 1995), pp. 231–238
L.I. Kuncheva, C.J. Whitaker, Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51(2), 181–207 (2003)
Y. Freund, R.E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
L. Breiman, Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
D.H. Wolpert, Stacked generalization. Neural Networks 5(2), 241–260 (1992)
L. Breiman, Random forests. Mach. Learn. 45(1), 5–32 (2001)
E. Bauer, R. Kohavi, An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Mach. Learn. 36(1–2), 105–139 (1999)
K.M. Ting, I.H. Witten, Issues in stacked generalization. J. Artif. Intell. Res. 10, 271–289 (1999)
D. Opitz, R. Maclin, Popular ensemble methods: an empirical study. J. Artif. Intell. Res. 11, 169–198 (1999)
Z.H. Zhou, J. Wu, W. Tang, Ensembling neural networks: many could be better than all. Artif. Intell. 137(1–2), 239–263 (2002)
A. Strehl, J. Ghosh, Cluster ensembles – a knowledge reuse framework for combining multiple partitionings. J. Mach. Learn. Res. 3, 583–617 (2002)
T.G. Dietterich, Machine learning research: four current directions. AI Mag. 18(4), 97–136 (1997)
Z.H. Zhou, Y. Jiang, S.F. Chen, Extracting symbolic rules from trained neural network ensembles. AI Commun. 16(1), 3–15 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer Science+Business Media New York
About this entry
Cite this entry
Zhou, ZH. (2015). Ensemble Learning. In: Li, S.Z., Jain, A.K. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7488-4_293
Download citation
DOI: https://doi.org/10.1007/978-1-4899-7488-4_293
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4899-7487-7
Online ISBN: 978-1-4899-7488-4
eBook Packages: Computer ScienceReference Module Computer Science and Engineering