广西师范大学学报（自然科学版） ›› 2022, Vol. 40 ›› Issue (4): 1-21.doi: 10.16088/j.issn.1001-6600.2021071102

• 综述 •    下一篇

面向复杂数据流的集成分类综述

张喜龙, 韩萌*, 陈志强, 武红鑫, 李慕航   

  1. 北方民族大学计算机科学与工程学院,宁夏银川 750021
  • 发布日期:2022-08-05
  • 通讯作者: 韩萌(1982—), 女(回族), 河南商丘人, 北方民族大学教授, 博士。E-mail: 2003051@nmu.edu.cn
  • 基金资助:
    国家自然科学基金(62062004); 宁夏自然科学基金(2020AAC03216,2022AAC03279); 北方民族大学研究生创新项目(YCX21085)

Survey of Ensemble Classification Methods for Complex Data Stream

ZHANG Xilong, HAN Meng*, CHEN Zhiqiang, WU Hongxin, LI Muhang   

  1. School of Computer Science and Engineering, North Minzu University, Yinchuan Ninxia 750021, China
  • Published:2022-08-05

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摘要： 随着大数据的快速发展,挖掘有价值的知识可能会面临高维、大量、动态数据的影响,这些复杂数据流的出现会导致分类效果下降。为了进一步分析数据流集成分类的研究现状和面临的挑战,本文对复杂数据流集成分类进行综述以供进一步研究,从复杂数据流、领域数据流角度重点介绍了目前算法的核心思想以及性能。其中,复杂数据流主要介绍概念漂移、不平衡、多标签数据流等;然后,介绍文本、图、传感器等领域数据流,归纳了集成学习在领域数据流中的应用;之后,从验证技术、评估指标方面对数据流评估方法进行介绍;最后,展望未来研究可能的几个方向,包括不确定数据流集成分类的挑战、多种数据流并存集成分类的挑战、延迟数据流集成分类的挑战、数据流分类评估方法的挑战等。

关键词: 复杂数据流, 集成学习, 评估方法, 领域数据流

Abstract: With rapid development of big data, mining valuable knowledge may face the impact of high-dimensional, large and dynamic data. The emergence of these complex data streams results in a decline in the classification effect. In order to further analyze the research status and challenges of data stream ensemble classification, the ensemble classification of complex data streams is reviewed for further research. For the first time, it focuses on the core ideas and performance of the algorithm from the perspective of complex data stream and domain data stream; among them, complex data stream mainly introduces concept drift, imbalance, multi-label data stream, etc. Then, text, graph, sensor are introduced, and the application of ensemble learning in domain data streamare sumarizes. Furthermore, data stream evaluation method is introduced from the aspects of verification technology and evaluation indicators. Finally, the future research direction is given, including challenges of the ensemble and classification of uncertain data stream, challenges of the ensemble and classification of multiple data stream, challenges of the delayed data stream and the ensemble classification, and challenges of data stream classification and evaluation methods.

Key words: complex data stream, ensemble learning, evaluation technology, domain data stream

中图分类号: 

  • TP181
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