Research to the Universal Ensemble Learning Approaches Based on Adaboost Algorithm

Authors

  • Li Xiaolong Jiangxi Institute of Fashion Technology Nanchang, Jiangxi, China
  • Chen Juanfen Jiangxi Institute of Fashion Technology Nanchang, Jiangxi, China
  • Liao Shiqin Jiangxi Institute of Fashion Technology Nanchang, Jiangxi, China

Abstract

AdaBoost which is one of the most outstanding Boosting algorithms belonging to machine learning too, has a steady theoretical basis and has made great progress to the problem solving. AdaBoost can boost a weak learning algorithm with an accuracy slightly better than random guessing into an arbitrarily accurate strong learning algorithm, bringing about a new method and a new design idea to the design of universal learning approaches.This paper first introduces how AdaBoost, just a conjecture when proposed, was proved right, and how this proof led to the origin of AdaBoost algorithm, reviewing the development history of ensemble learning,and focuses on the three strategies of diversity generation, model training and model combination in ensemble learning, and then describes the relevant application scenarios of ensemble learning at the current stage. Finally, the future research direction of ensemble learning approaches is analyzed and discovered.

Keywords: Ensemble learning, AdaBoost algorithm, Boosting, Machine learning

Cite As

L. Xiaolong, C. Juanfen, L. Shiqin, "Research to the Universal Ensemble Learning Approaches
Based on Adaboost Algorithm", Engineering Intelligent Systems, vol. 26 no. 4, pp. 191-197, 2018.




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Published

2018-12-01

Issue

Vol. 26 No. 4 (2018): International Journal of Engineering Intelligent Systems

Section

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