Name
Abstract | ||
|---|---|---|
Ensemble methods that train multiple learners and then combine their predictions have been shown to be very effective in supervised learning. But bagging is not work very well in some case, such as k-Nearest Neighbor (kNN). At the same time, Query Learning Strategies using Bagging [1] is also not work very well. From features view, we introduce bagging features active learning (ALBF) for kNN and apply this method to ML-kNN. Experiments in UCI data set show that prediction accuracy could be significantly improved by ALBF. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICNC.2008.868 | ICNC |
Keywords | Field | DocType |
query learning strategies,k-nearest neighbor,features view,prediction accuracy,bagging feature,active learning,ensemble method,uci data,supervised learning,multiple learner,bagging features,classification algorithms,ensemble methods,machine learning,k nearest neighbor,bagging,learning artificial intelligence,training data,knn | k-nearest neighbors algorithm,Training set,Query learning,Active learning,Computer science,Supervised learning,Artificial intelligence,Statistical classification,Ensemble learning,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.36 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuo Shi | 1 | 10 | 6.43 |
| Yuhai Liu | 2 | 8 | 3.24 |
| Yuehua Huang | 3 | 207 | 13.11 |
| Shihua Zhu | 4 | 1 | 0.36 |
| Yong Liu | 5 | 1 | 0.36 |