Name
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74825-0_12 | IDA |
Keywords | Field | DocType |
major concern,out-of-bag estimation,bagging algorithm,regression problem,ensemble model,algorithms learner,neural networks,neural network,ensemble methods,bagging,ensemble learning | Computer science,Robustness (computer science),Bootstrap aggregating,Artificial intelligence,Overfitting,Artificial neural network,Ensemble learning,Visibility,Ensemble forecasting,Pattern recognition,Algorithm,Regression problems,Machine learning | Conference |
Volume | ISSN | Citations |
4723 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos Valle | 1 | 21 | 8.20 |
| Ricardo Ñanculef | 2 | 53 | 10.64 |
| Héctor Allende | 3 | 148 | 31.69 |
| Claudio Moraga | 4 | 612 | 100.27 |