Chinese Journal of Intelligent Science and Technology ›› 2021, Vol. 3 ›› Issue (1): 18-35.doi: 10.11959/j.issn.2096-6652.202103

• Surveys and Prospectives • Previous Articles     Next Articles

A survey on evolutionary ensemble learning algorithm

Yi HU1, Boyang QU2, Jing LIANG1, Jie WANG1, Yanli WANG1   

  1. 1 School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
    2 School of Electronic and Information, Zhongyuan University of Technology, Zhengzhou 450007, China
  • Revised:2020-08-25 Online:2021-03-15 Published:2021-03-01
  • Supported by:
    The National Natural Science Foundation of China(61922072);The Key Scientific Research Projects of Henan Province under Grant(18A470015)


Evolutionary ensemble learning integrates advantages of ensemble learning and evolutionary algorithm and is widely used in machine learning, data mining, and pattern recognition.Firstly, the theoretical basis, formation, and taxonomy are introduced.Secondly, according to the optimization task of evolutionary algorithm in ensemble learning, some representative researches on evolutionary ensemble learning field were analysed from the aspects of instance selection, feature selection, parameter optimization, structure optimization, and fusion strategy optimization, and the characteristics of different evolutionary ensemble learning methods were summarized.Finally, the pros and cons of the current researches on evolutionary ensemble learning were analysed, and research directions in the future work were given.

Key words: ensemble learning, machine learning, evolutionary algorithm, classification, regression, clustering

CLC Number: 

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