Two bagging algorithms with coupled learners to encourage diversity. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 4723 LNCS, pages 130-139, 2007. abstract bibtex 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. © Springer-Verlag Berlin Heidelberg 2007.
@inproceedings{38049056650,
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. © Springer-Verlag Berlin Heidelberg 2007.",
year = "2007",
title = "Two bagging algorithms with coupled learners to encourage diversity",
volume = "4723 LNCS",
pages = "130-139",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"
}
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