Name
Abstract | ||
|---|---|---|
Ensemble methods have proved to be highly effective in improving the performance of base learners under most circumstances. In this paper, we propose a new algorithm that combines the merits of some existing techniques, namely bagging, arcing and stacking. The basic structure of the algorithm resembles bagging. However, the misclassification cost of each training point is repeatedly adjusted according to its observed out-of-bag vote margin. In this way, the method gains the advantage of arcing - building the classifier the ensemble needs - without fixating on potentially noisy points. Computational experiments show that this algorithm performs consistently better than bagging and arcing with linear and nonlinear base classifiers. In view of the characteristics of bacing, a hybrid ensemble learning strategy, which combines bagging and different versions of bacing, is proposed and studied empirically. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TKDE.2007.190724 | IEEE Trans. Knowl. Data Eng. |
Keywords | Field | DocType |
basic structure,hybrid ensemble,ensemble need,existing technique,adaptive costs,ensemble method,nonlinear base classifier,new algorithm,computational experiment,different version,base learner,data mining,boosting,bagging,ensemble learning,computer experiment,learning artificial intelligence,stacking,ensemble methods,voting | Data mining,Nonlinear system,Computer science,Adaptive method,Information extraction,Boosting (machine learning),Artificial intelligence,Classifier (linguistics),Ensemble learning,Machine learning | Journal |
Volume | Issue | ISSN |
20 | 5 | 1041-4347 |
ISBN | Citations | PageRank |
0-7695-2278-5 | 2 | 0.39 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Zhang | 1 | 214 | 10.52 |
| W. Nick Street | 2 | 1828 | 155.26 |