Title :
A revised AdaBoost algorithm: FM-AdaBoost
Author :
Zhang, Yanfeng ; He, Peikun
Author_Institution :
Sch. of Inf. & Electron., Beijing Inst. of Technol., Beijing, China
Abstract :
In view of ensemble equivalence, this paper proposes a revised AdaBoost algorithm: FM-AdaBoost. It can ensure the ensemble error rates are the least by F-module, which filter classifiers after all of the iteration finish. At the same time, with the optional M-module it can ensure the training error rates decreases monotonously, which improves the training velocity effectively. In the end, simulation results show the algorithm is valid.
Keywords :
iterative methods; learning (artificial intelligence); pattern classification; F-module; FM AdaBoost; ensemble error rate; filter classifiers; iteration finish; optional M-module; revised AdaBoost algorithm; training error rates; Accuracy; Algorithm design and analysis; Boosting; Classification algorithms; Error analysis; Training; AdaBoost; classifier; ensemble of classifiers;
Conference_Titel :
Computer Application and System Modeling (ICCASM), 2010 International Conference on
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4244-7235-2
Electronic_ISBN :
978-1-4244-7237-6
DOI :
10.1109/ICCASM.2010.5623209
