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Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography

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  • Rokach, Lior

Abstract

Ensemble methodology, which builds a classification model by integrating multiple classifiers, can be used for improving prediction performance. Researchers from various disciplines such as statistics, pattern recognition, and machine learning have seriously explored the use of ensemble methodology. This paper presents an updated survey of ensemble methods in classification tasks, while introducing a new taxonomy for characterizing them. The new taxonomy, presented from the algorithm designer's point of view, is based on five dimensions: inducer, combiner, diversity, size, and members' dependency. We also propose several selection criteria, presented from the practitioner's point of view, for choosing the most suitable ensemble method.

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  • Rokach, Lior, 2009. "Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4046-4072, October.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:12:p:4046-4072
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    1. Merler, Stefano & Caprile, Bruno & Furlanello, Cesare, 2007. "Parallelizing AdaBoost by weights dynamics," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2487-2498, February.
    2. Adem, Jan & Gochet, Willy, 2004. "Aggregating classifiers with mathematical programming," Computational Statistics & Data Analysis, Elsevier, vol. 47(4), pages 791-807, November.
    3. Croux, Christophe & Joossens, Kristel & Lemmens, Aurelie, 2007. "Trimmed bagging," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 362-368, September.
    4. Hothorn, Torsten & Lausen, Berthold, 2005. "Bundling classifiers by bagging trees," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1068-1078, June.
    5. Archer, Kellie J. & Kimes, Ryan V., 2008. "Empirical characterization of random forest variable importance measures," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2249-2260, January.
    6. Drucker, Harris, 2002. "Effect of pruning and early stopping on performance of a boosting ensemble," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 393-406, February.
    7. Buttrey, Samuel E. & Karo, Ciril, 2002. "Using k-nearest-neighbor classification in the leaves of a tree," Computational Statistics & Data Analysis, Elsevier, vol. 40(1), pages 27-37, July.
    8. Sexton, Joseph & Laake, Petter, 2008. "LogitBoost with errors-in-variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2549-2559, January.
    9. Kim, Yuwon & Koo, Ja-Yong, 2005. "Inverse boosting for monotone regression functions," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 757-770, June.
    10. Tsao, C. Andy & Chang, Yuan-chin Ivan, 2007. "A stochastic approximation view of boosting," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 325-334, September.
    11. Christmann, Andreas & Steinwart, Ingo & Hubert, Mia, 2007. "Robust learning from bites for data mining," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 347-361, September.
    12. Denison, D. G. T. & Adams, N. M. & Holmes, C. C. & Hand, D. J., 2002. "Bayesian partition modelling," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 475-485, February.
    13. Gey, Servane & Poggi, Jean-Michel, 2006. "Boosting and instability for regression trees," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 533-550, January.
    14. Ahn, Hongshik & Moon, Hojin & Fazzari, Melissa J. & Lim, Noha & Chen, James J. & Kodell, Ralph L., 2007. "Classification by ensembles from random partitions of high-dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6166-6179, August.
    15. Moskovitch, Robert & Elovici, Yuval & Rokach, Lior, 2008. "Detection of unknown computer worms based on behavioral classification of the host," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4544-4566, May.
    16. Menahem, Eitan & Shabtai, Asaf & Rokach, Lior & Elovici, Yuval, 2009. "Improving malware detection by applying multi-inducer ensemble," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1483-1494, February.
    17. Zhang, Chun-Xia & Zhang, Jiang-She, 2008. "A local boosting algorithm for solving classification problems," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1928-1941, January.
    18. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    19. Rokach, Lior, 2009. "Collective-agreement-based pruning of ensembles," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1015-1026, February.
    20. Ridgeway, Greg, 2002. "Looking for lumps: boosting and bagging for density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 379-392, February.
    21. Yuval Elovici & Bracha Shapira & Paul B. Kantor, 2006. "A decision theoretic approach to combining information filters: An analytical and empirical evaluation," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 306-320, February.
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    8. Mojirsheibani, Majid & Kong, Jiajie, 2016. "An asymptotically optimal kernel combined classifier," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 91-100.
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