Title :
Research on AdaBoost.M1 with Random Forest
Author :
Zhang, Zhenyu ; Xie, Xiaoyao
Author_Institution :
Dept. of Dev. Strategy, China Mobile Group Guizhou Co., Ltd., Guiyang, China
Abstract :
The AdaBoost.M1 is one of the machine learning algorithms. But it will fail if the weak learner cannot achieve at least 50% accuracy when run on these hard distributions. Random Forest is computationally effective and offer good prediction performance. A new approach AdaBoost.M1-RF algorithm, which using Random Forest as weak learner, is proposed in the paper. To evaluate the performance of AdaBoost.M1-RF algorithm, it is compared with other machine learning algorithms.
Keywords :
decision trees; learning (artificial intelligence); AdaBoost.Ml-RF algorithm; AdaBoostMl; machine learning algorithms; random forest; Boosting; Classification tree analysis; Game theory; Laboratories; Machine learning; Machine learning algorithms; Neural networks; Robustness; Upper bound; Virtual colonoscopy; AdaBoost; AdaBoost.M1; AdaBoost.M1-RF; Random Forest;
Conference_Titel :
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-6347-3
DOI :
10.1109/ICCET.2010.5485910
