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TS-DM:一种基于时间分割的数据流学习方法,用于概念漂移适应

Kun Wang, Jie Lu, Anjin Liu

IEEE transactions on cybernetics

|August 12, 2024

概括

本研究引入了一种新的基于时间分割的数据流学习方法 (TS-DM),以有效地处理机器学习中的概念漂移. 该方法通过智能细分和学习流数据来提高模型准确性,防止知识丢失.

科学领域:

机器学习机器学习
数据挖掘数据挖掘
人工智能的人工智能

背景情况:

概念漂移是数据流中的一个常见挑战,原因是数据分布的不断变化.
现有的方法在概念漂移期间经常难以最佳地管理数据样本,导致潜在的知识损失和模型精度降低.

研究的目的:

为有效的概念漂移适应提出基于时间分割的新型数据流学习方法 (TS-DM).
提高运行在流数据上的机器学习模型的概括性和稳定性.

主要方法:

开发了一个基于块的细分策略,以区分正常和漂移数据块.
引入了基于块的不断演变的细分 (CES) 策略,以挖掘和细分旧和新概念共存的数据.
实施了警告级数据细分流程 (CES-W) 和高低漂移权衡处理流程.

主要成果:

在合成和现实数据集上的实验评估证明了TS-DM方法的效率.
与一些最先进的数据流学习技术相比,提出的方法显示出更高的性能.
TS-DM有效地解决了在概念漂移过程中不当保存或丢弃数据样本的挑战.

结论:

TS-DM方法为数据流中的概念漂移适应提供了一种高效和强大的方法.
拟议的细分和权衡策略增强了模型从不断变化的数据中学习的能力.
这项工作有助于提高机器学习模型在动态环境中的准确性和可靠性.

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