TY - GEN
T1 - Active learning for kNN based on bagging features
AU - Shuo, Shi
AU - Yuhai, Liu
AU - Yuehua, Huang
AU - Shihua, Zhu
AU - Yong, Liu
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/57649203403
U2 - 10.1109/ICNC.2008.868
DO - 10.1109/ICNC.2008.868
M3 - 会议稿件
AN - SCOPUS:57649203403
SN - 9780769533049
T3 - Proceedings - 4th International Conference on Natural Computation, ICNC 2008
SP - 61
EP - 64
BT - Proceedings - 4th International Conference on Natural Computation, ICNC 2008
T2 - 4th International Conference on Natural Computation, ICNC 2008
Y2 - 18 October 2008 through 20 October 2008
ER -