Abstract
In this paper, we present two ensemble learning algorithms which make use of boostrapping and out-of-bag estimation in an attempt to inherit the robustness of bagging to overfitting. As against bagging, with these algorithms learners have visibility on the other learners and cooperate to get diversity, a characteristic that has proved to be an issue of major concern to ensemble models. Experiments are provided using two regression problems obtained from UCI.
This work was supported in part by Research Grant Fondecyt (Chile) 1040365, 7060040 and 1070220. Partial support was also received from Research Grant 2407265 DGIP-UTFSM (Chile).
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Valle, C., Ñanculef, R., Allende, H., Moraga, C. (2007). Two Bagging Algorithms with Coupled Learners to Encourage Diversity. In: R. Berthold, M., Shawe-Taylor, J., Lavrač, N. (eds) Advances in Intelligent Data Analysis VII. IDA 2007. Lecture Notes in Computer Science, vol 4723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74825-0_12
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DOI: https://doi.org/10.1007/978-3-540-74825-0_12
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