进化集成学习算法综述

胡毅; 瞿博阳; 梁静; 王杰; 王艳丽

doi:10.11959/j.issn.2096-6652.202103

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进化集成学习算法综述

综述与展望 | 更新时间：2024-06-05

- 进化集成学习算法综述
- A survey on evolutionary ensemble learning algorithm
- 智能科学与技术学报 2021年3卷第1期页码：18-35
- 作者机构：
  
  1. 郑州大学电气工程学院，河南郑州 450001
  2. 中原工学院电子信息学院，河南郑州 450007
- 作者简介：
  
  [ "胡毅（1988- ），男，郑州大学电气工程学院博士生，主要研究方向为进化计算、机器学习、智能控制及其应用。" ]
  [ "瞿博阳（1984- ），男，博士，中原工学院电子信息学院教授、院长，主要研究方向为机器学习、遗传与进化算法、群集智能和多目标优化。" ]
  [ "梁静（1981- ），女，博士，郑州大学电气工程学院教授、院长，主要研究方向为计算智能、智能优化及机器学习。" ]
  [ "王杰（1959- ），男，博士，郑州大学电气工程学院教授，主要研究方向为模式识别与智能控制。" ]
  [ "王艳丽（1982- ），女，郑州大学电气工程学院博士生，主要研究方向为智能计算和机器学习。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61922072;61976237;61876169;61673404;61806179);河南省高等学校重点科研资助项目;The Key Scientific Research Projects of Henan Province under Grant(18A470015;20B120002);中国博士后科学基金资助项目(2017M622373;2018M630835)
- DOI：10.11959/j.issn.2096-6652.202103
  中图分类号： TP181
- 网络出版日期：2021-03，
  
  纸质出版日期：2021-03-15
- 稿件说明：
移动端阅览
胡毅, 瞿博阳, 梁静, 等. 进化集成学习算法综述[J]. 智能科学与技术学报, 2021,3(1):18-35.

Yi HU, Boyang QU, Jing LIANG, et al. A survey on evolutionary ensemble learning algorithm[J]. Chinese journal of intelligent science and technology, 2021, 3(1): 18-35.
胡毅, 瞿博阳, 梁静, 等. 进化集成学习算法综述[J]. 智能科学与技术学报, 2021,3(1):18-35. DOI： 10.11959/j.issn.2096-6652.202103.

Yi HU, Boyang QU, Jing LIANG, et al. A survey on evolutionary ensemble learning algorithm[J]. Chinese journal of intelligent science and technology, 2021, 3(1): 18-35. DOI： 10.11959/j.issn.2096-6652.202103.

摘要

进化集成学习结合了集成学习和进化算法两方面的优势，并在机器学习、数据挖掘和模式识别等领域被广泛应用。首先对进化集成学习算法的理论基础、组成结构及分类情况进行了概述。然后根据进化算法在集成学习中的优化任务，从样本选择、特征选择、集成模型参数组合优化、集成模型结构优化以及集成模型融合策略优化几个方面对当前进化集成学习领域的一些代表性研究成果进行了详细的综述，并分析和总结了各种进化集成学习算法的特点。最后对现有的进化集成研究的优缺点进行探讨，并给出了未来的研究方向。

Abstract

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.

关键词

Keywords

references

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