Abstract
In this paper, we confirm the effects of ensemble learning approaches in classification problems based on rough set. Furthermore, we propose an ensemble learning approach based on rough set preserving the qualities of approximations. The proposed method stands on a policy that subsets of attributes whose quality of lower approximation is less than the threshold value is not tolerate. We carried out numerical experiments in order to confirm the classification performance of the proposed method and confirmed its effectiveness.
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References
Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)
Breiman, L.: Random Forests. Machine Learning 45, 5–32 (2001)
Grzymala-Busse, J.W., Slowinski, R. (ed.): LERS-A System for Learning from Examples Based on Rough Sets. Handbook of Application and Advances of the Rough Set Theory, vol. 11, pp. 3–18. Kluwer Cademic Publishers (1992)
Grzymala-Busse, J.W.: MLEM2-discretization during rule induction. In: Proceeding of International Conference on Intelligent Information Processing and WEB Mining Systems, vol. 22, pp. 499–508 (2003)
Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11, 5 (1982)
UCI machine learning repository. http://archive.ics.uci.edu/ml/
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Ubukata, S., Miyazaki, T., Notsu, A., Honda, K., Inuiguchi, M. (2015). An Ensemble Learning Approach Based on Rough Set Preserving the Qualities of Approximations. In: Huynh, VN., Inuiguchi, M., Demoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2015. Lecture Notes in Computer Science(), vol 9376. Springer, Cham. https://doi.org/10.1007/978-3-319-25135-6_24
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DOI: https://doi.org/10.1007/978-3-319-25135-6_24
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