Abstract
As mentioned in Chap. 1, ensemble learning is helpful to improve overall accuracy of classification. This chapter introduces three approaches of ensemble learning namely, parallel learning, sequential learning and hybrid learning. In particular, some popular methods for ensemble learning, such as Bagging and Boosting, are illustrated in detail. These methods are also discussed comparatively with respects to their advantages and disadvantages.
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References
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Kononenko, I., Kukar, M.: Machine Learning and Data Mining: Introduction to Principles and Algorithms, Chichester. Horwood Publishing Limited, West Sussex (2007)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Education Inc, New Jersey (2006)
Breiman, L.: Random Forests. Mach. Learn. 45(1), 5–32 (2001)
Li, J., Wong, L.: Rule based data mining methods for classification problems in biomedical domains. In: 15th European Conference on Machine Learning and 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, Pisa (2004)
Liu, H., Gegov, A.: Collaborative decision making by ensemble rule based classification systems. In: Pedrycz, W., Chen, S. (eds.) Granular Computing and Decision-Making: Interactive and Iterative Approaches, vol. 10, pp. 245–264. Springer, (2015)
Freund, Y., Schapire, R.E.: A short introduction to boosting. J. Japan. Soc. Artif. Intell. 14(5), 771–780 (1999)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Machine Learning: Proceedings of the Thirteenth International Conference, Bari (1996)
Liu, H., Gegov, A., Cocea, M.: Hybrid ensemble learning approach for generation of classification rules. In: International Conference on Machine Learning and Cybernetics, Guangzhou (2015)
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Liu, H., Gegov, A., Cocea, M. (2016). Ensemble Learning Approaches. In: Rule Based Systems for Big Data. Studies in Big Data, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-23696-4_6
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DOI: https://doi.org/10.1007/978-3-319-23696-4_6
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