基于动态样本选择的概念漂移自适应预测方法

代劲; 李昊; 王国胤

doi:10.12263/DZXB.20230124

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基于动态样本选择的概念漂移自适应预测方法

学术论文 | 更新时间：2025-12-08

- 基于动态样本选择的概念漂移自适应预测方法
- Concept Drift Adaptive Prediction Method Based on Dynamic Sample Selection
- 电子学报 2024年52卷第9期页码：3228-3239
- 作者机构：
  
  1.重庆邮电大学软件学院，重庆 400065
  2.重庆邮电大学计算机学院，重庆 400065
  3.计算智能重庆市重点实验室，重庆 400065
- 作者简介：
  
  [ "代劲男，1978年出生.教授，博士，从事大数据知识工程、智能信息处理等研究.E-mail: daijin@cqupt.edu.cn" ]
  [ "李昊男，1996年出生，硕士研究生，主要研究领域为机器学习，流数据挖掘.E-mail: S200201023@stu.cqupt.edu.cn" ]
  [ "王国胤男，1970出生.教授，博士生导师，从事粒计算主要研究领域为多粒度认知计算、认知计算、智能信息处理等研究. E-mail: wanggy@cqupt.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(61936001;62002037);重庆市自然科学基金(cstc2021jcyj-msxmX0849;cstb2023nscq-LZX0006)
- DOI：10.12263/DZXB.20230124
  中图分类号： TP311
- 收稿：2023-02-10，
  
  修回：2023-07-29，
  
  纸质出版：2024-09-25
- 稿件说明：
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代劲, 李昊, 王国胤. 基于动态样本选择的概念漂移自适应预测方法[J]. 电子学报, 2024, 52(09): 3228-3239.

DAI Jin, LI Hao, WANG Guo-yin. Concept Drift Adaptive Prediction Method Based on Dynamic Sample Selection[J]. Acta Electronica Sinica, 2024, 52(09): 3228-3239.
代劲, 李昊, 王国胤. 基于动态样本选择的概念漂移自适应预测方法[J]. 电子学报, 2024, 52(09): 3228-3239. DOI：10.12263/DZXB.20230124

DAI Jin, LI Hao, WANG Guo-yin. Concept Drift Adaptive Prediction Method Based on Dynamic Sample Selection[J]. Acta Electronica Sinica, 2024, 52(09): 3228-3239. DOI：10.12263/DZXB.20230124

摘要

概念漂移是影响流数据挖掘性能的重要因素，当前主要通过增量更新或重训练模型进行处理，但对已有知识并未充分利用.从综合利用全体样本出发，本文构建了一种基于动态样本选择的概念漂移自适应分类方法.该方法在新样本到来时进行基于局部一致性的漂移检测，在发现漂移发生时去除区域内的噪声样本，当检测到新概念出现时，对历史相似概念进行重用.最后，对区域内不同类别样本进行多代表点归纳，并同步更新预测模型.本文在含有不同漂移类型的合成数据集上进行去噪效果验证，并在真实数据集上进行预测任务.实验结果表明，该方法可以有效去除因概念漂移而形成的漂移噪声，有效提升了预测模型性能，整体预测表现优于流行的概念漂移自适应模型.

Abstract

Concept drift is an important performance factor in stream data mining

mainly handled by incremental updating or retraining models

but not fully utilizing existing knowledge. This paper proposed an concept drift adaptive prediction method based on dynamic sample selection

starting from the comprehensive use of all samples. The method performs local consistency based drift detection when new samples arrive

removes noisy samples in the region when drift is detected

and reuses historically similar concepts when new concepts are detected. Finally

multi-representative point summarization is performed for different categories of samples in the region

and the prediction model is updated simultaneously. In this paper

the denoising effect is verified on synthetic datasets containing different drift types

and the prediction task is performed on the real dataset. The experimental results show that the method can effectively remove the drift noise due to conceptual drift

which effectively improves the performance of the prediction model. The prediction outperforms the popular concept drift adaptive model.

关键词

Keywords

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