多元模式,统一目标:集成学习的综合研究

IF 7.3 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ziwei Fan, Zhiwen Yu, Kaixiang Yang, Wuxing Chen, Xiaoqing Liu, Guojie Li, Xianling Yang, C. L. Philip Chen
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引用次数: 0

摘要

集成学习是机器学习的一个关键分支,它整合了多个基本模型来增强预测模型的总体性能,利用集成的多样性和集体智慧来超越单个模型并减轻过拟合。本文为集成学习的研究建立了一个四层的研究框架,可以从下到上对集成学习进行全面、结构化的回顾。首先,本研究首先介绍了基本的集成学习技术,包括bagging、boosting和stacking,同时也探索了集成的多样性。然后详细研究了深度集成学习和半监督集成学习。此外,还讨论了利用集成学习技术来导航具有挑战性的数据集,如不平衡和高维数据。还研究了集成学习技术在各种研究领域的应用,包括医疗保健、交通运输、金融、制造业和互联网。调查最后讨论了集成学习的内在挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Diverse Models, United Goal: A Comprehensive Survey of Ensemble Learning

Diverse Models, United Goal: A Comprehensive Survey of Ensemble Learning

Diverse Models, United Goal: A Comprehensive Survey of Ensemble Learning

Diverse Models, United Goal: A Comprehensive Survey of Ensemble Learning

Ensemble learning, a pivotal branch of machine learning, amalgamates multiple base models to enhance the overarching performance of predictive models, capitalising on the diversity and collective wisdom of the ensemble to surpass individual models and mitigate overfitting. In this review, a four-layer research framework is established for the research of ensemble learning, which can offer a comprehensive and structured review of ensemble learning from bottom to top. Firstly, this survey commences by introducing fundamental ensemble learning techniques, including bagging, boosting, and stacking, while also exploring the ensemble's diversity. Then, deep ensemble learning and semi-supervised ensemble learning are studied in detail. Furthermore, the utilisation of ensemble learning techniques to navigate challenging datasets, such as imbalanced and high-dimensional data, is discussed. The application of ensemble learning techniques across various research domains, including healthcare, transportation, finance, manufacturing, and the Internet, is also examined. The survey concludes by discussing challenges intrinsic to ensemble learning.

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来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
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