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    胡阳, 孙自强. 基于McDiarmid边界的自适应加权概念漂移检测方法[J]. 华东理工大学学报(自然科学版), 2023, 49(3): 419-428. DOI: 10.14135/j.cnki.1006-3080.20211215002
    引用本文: 胡阳, 孙自强. 基于McDiarmid边界的自适应加权概念漂移检测方法[J]. 华东理工大学学报(自然科学版), 2023, 49(3): 419-428. DOI: 10.14135/j.cnki.1006-3080.20211215002
    HU Yang, SUN Ziqiang. Adaptive Weighted Concept Drift Detection Method Based on McDiarmid Boundary[J]. Journal of East China University of Science and Technology, 2023, 49(3): 419-428. DOI: 10.14135/j.cnki.1006-3080.20211215002
    Citation: HU Yang, SUN Ziqiang. Adaptive Weighted Concept Drift Detection Method Based on McDiarmid Boundary[J]. Journal of East China University of Science and Technology, 2023, 49(3): 419-428. DOI: 10.14135/j.cnki.1006-3080.20211215002

    基于McDiarmid边界的自适应加权概念漂移检测方法

    Adaptive Weighted Concept Drift Detection Method Based on McDiarmid Boundary

    • 摘要: 针对概念漂移主动检测方法检测延迟高,易出现漏检、误报的问题,提出了一种基于McDiarmid边界的自适应加权概念漂移检测方法。引入衰减函数对分类结果加权,赋予旧数据更低权值,提升新数据的影响力。利用McDiarmid不等式得到加权分类正确率的置信边界,在检测到分类正确率下降超过置信边界时调节衰减因子时,实现权值的动态改变。实验主要与DDM(Drift Detection Method)、RDDM(Reactive Drift Detection Method)、HDDM(Drift Detection Method based on the Hoeffding's inequality)、FHDDM(Fast Hoeffding Drift Detection Method)和窗口(ADWIN)算法对比,结果表明,该算法具有最低的误报率和漏检率,且平均检测延迟和正确率在6种算法中排前2。

       

      Abstract: Aiming at the shortcomings that the active detection method of concept drift is subject to high detection delay, missed detection, and false alarm, this paper proposes an adaptive weighted concept drift detection method based on McDiarmid boundary (WMDDM). The proposed algorithm has a weighted adjustment mechanism. The adaptive attenuation algorithm is introduced as a weight function to give old data lower weights that are dynamically adjusted to adapt to the concept drift faster according to the changes in the data stream. McDiarmid's inequality is utilized to obtain the warning level and drift level of the weighted classification accuracy. While the weighted classification accuracy rate is detected to drop outside the drift level, the detection result is fed back to the classifier. While it is detected that the weighted classification accuracy rate drops beyond the warning level, the detector adapts to the change of the data flow by the triggered weight adjustment mechanism. Finally, the experiments are made on 4 artificial data sets and 1 real data set by the comparison with Fast Hoeffding Drift Detection Method (FHDDM), Drift Detection Method based on Hoeffding’s inequality (HDDM) and other algorithms. It is shown via experimental results that the proposed WMDDM algorithm has the lowest false alarm rate and missed detection rate, and its average detection delay and accuracy rate rank second among six algorithms. In addition, the proposed WMDDM algorithm is also used to classify real data sets by comparing with FHDDM algorithm, from which it is shown that WMDDM algorithm has a higher classification accuracy rate than FHDDM. Therefore, the WMDDM algorithm is more suitable for abrupt and gradual conceptual drift, and has strong robustness.

       

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