Ensemble learning: A survey

Omer Sagi; Lior Rokach

doi:10.1002/widm.1249

journal article Feb 27, 2018

Ensemble learning: A survey

Omer Sagi Lior Rokach

WIREs Data Mining and Knowledge Discovery Vol. 8 No. 4 · Wiley

View at Publisher Save 10.1002/widm.1249

Abstract

Ensemble methods are considered the state‐of‐the art solution for many machine learning challenges. Such methods improve the predictive performance of a single model by training multiple models and combining their predictions. This paper introduce the concept of ensemble learning, reviews traditional, novel and state‐of‐the‐art ensemble methods and discusses current challenges and trends in the field.This article is categorized under:

Algorithmic Development > Ensemble Methods
Technologies > Machine Learning
Technologies > Classification

Topics

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References

154

[1]

10.1109/tkde.2013.134

[2]

Ali K. M. (1995)

[3]

Amit Y. Geman D.(1994).Randomized inquiries about shape: An application to handwritten digit recognition[DTIC document].

[4]

Avidan S. "Spatialboost: Adding spatial reasoning to adaboost" Computer Vision—ECCV (2006)

[5]

10.1016/j.patcog.2009.11.012

[6]

10.1016/j.inffus.2004.04.005

[7]

10.1109/tpami.2007.250609

[8]

An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Eric Bauer, Ron Kohavi

Machine Learning 10.1023/a:1007515423169

[9]

Beemer J. "Ensemble learning for estimating individualized treatment effects in student success studies" International Journal of Artificial Intelligence in Education (2017)

[10]

10.1142/9789812799623_0002

[11]

10.1016/j.patrec.2012.04.003

[12]

10.1016/j.ijar.2011.12.011

[13]

Bifet A. (2010)

[14]

10.1145/1557019.1557041

[15]

Blaser R. "Random rotation ensembles" Journal of Machine Learning Research (2016)

[16]

10.1007/978-3-319-47160-0_53

[17]

Bagging predictors

Leo Breiman

Machine Learning 10.1007/bf00058655

[18]

Random Forests

Leo Breiman

Machine Learning 10.1023/a:1010933404324

[19]

Breiman L. Shang N.(1996).Born again trees. Retrieved from ftp://ftp stat berkeleyedu/pub/users/breiman/BAtreesps

[20]

Brown G. "Managing diversity in regression ensembles" Journal of Machine Learning Research (2005)

[21]

10.1016/s0031-3203(02)00121-8

[22]

Model compression

Cristian Buciluǎ, Rich Caruana, Alexandru Niculescu-Mizil

Proceedings of the 12th ACM SIGKDD international c... 10.1145/1150402.1150464

[23]

Ensemble selection from libraries of models

Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew et al.

Twenty-first international conference on Machine l... 10.1145/1015330.1015432

[24]

10.1016/b978-1-55860-377-6.50020-7

[25]

Chan P. K. (1995)

[26]

Chang Y. "Unsupervised video shot detection using clustering ensemble with a color global scale‐invariant feature transform descriptor" Journal on Image and Video Processing (2008)

[27]

Chawla N. V. "Learning ensembles from bites: A scalable and accurate approach" Journal of Machine Learning Research (2004)

[28]

10.1145/2939672.2939785

[29]

Cortes C. (2014)

[30]

10.1016/j.knosys.2012.08.024

[31]

10.1109/proc.1979.11321

[32]

Deng H.(2014).Interpreting tree ensembles with intrees. arXiv preprint arXiv:14085456.

[33]

10.1016/j.ins.2013.02.030

[34]

Deng L. (2014)

[35]

10.1007/3-540-36755-1_6

[36]

An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

Thomas G. Dietterich

Machine Learning 10.1023/a:1007607513941

[37]

Dietterich T. G. (2002)

[38]

Solving Multiclass Learning Problems via Error-Correcting Output Codes

T. G. Dietterich, G. Bakiri

Journal of Artificial Intelligence Research 10.1613/jair.105

[39]

10.1016/s1088-467x(98)00023-7

[40]

10.1016/j.advwatres.2006.11.014

[41]

10.1145/2976749.2989055

[42]

Fernández‐Delgado M. "Do we need hundreds of classifiers to solve real world classification problems" Journal of Machine Learning Research (2014)

[43]

10.1109/icpr.2002.1047450

[44]

10.1109/tpami.2005.113

[45]

10.1145/2594473.2594475

[46]

A desicion-theoretic generalization of on-line learning and an application to boosting

Yoav Freund, Robert E. Schapire

Lecture Notes in Computer Science 10.1007/3-540-59119-2_166

[47]

Greedy function approximation: A gradient boosting machine.

Jerome H. Friedman

The Annals of Statistics 10.1214/aos/1013203451

[48]

Stochastic gradient boosting

Jerome H. Friedman

Computational Statistics & Data Analysis 10.1016/s0167-9473(01)00065-2

[49]

A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches

M. Galar, A. Fernandez, E. Barrenechea et al.

IEEE Transactions on Systems, Man and Cybernetics,... 10.1109/tsmcc.2011.2161285

[50]

10.1016/j.patcog.2013.05.006

Showing 50 of 154 references

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Details

Published: Feb 27, 2018
Vol/Issue: 8(4)
License: View

Authors

O

Omer Sagi

Department of Software and Information Systems Engineering Ben‐Gurion University Beersheba Israel

L

Lior Rokach

Department of Software and Information Systems Engineering Ben‐Gurion University Beersheba Israel

Cite This Article

Omer Sagi, Lior Rokach (2018). Ensemble learning: A survey. WIREs Data Mining and Knowledge Discovery, 8(4). https://doi.org/10.1002/widm.1249

Ensemble learning: A survey

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