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非平衡数据流在线主动学习方法

李艳红 任霖 王素格 李德玉

李艳红, 任霖, 王素格, 李德玉. 非平衡数据流在线主动学习方法. 自动化学报, 2024, 50(7): 1389−1401 doi: 10.16383/j.aas.c211246
引用本文: 李艳红, 任霖, 王素格, 李德玉. 非平衡数据流在线主动学习方法. 自动化学报, 2024, 50(7): 1389−1401 doi: 10.16383/j.aas.c211246
Li Yan-Hong, Ren Lin, Wang Su-Ge, Li De-Yu. Online active learning method for imbalanced data stream. Acta Automatica Sinica, 2024, 50(7): 1389−1401 doi: 10.16383/j.aas.c211246
Citation: Li Yan-Hong, Ren Lin, Wang Su-Ge, Li De-Yu. Online active learning method for imbalanced data stream. Acta Automatica Sinica, 2024, 50(7): 1389−1401 doi: 10.16383/j.aas.c211246

非平衡数据流在线主动学习方法

doi: 10.16383/j.aas.c211246
基金项目: 国家自然科学基金(62076158, 62072294, 41871286), 山西省重点研发计划(201903D421041)资助
详细信息
    作者简介:

    李艳红:山西大学计算机与信息技术学院副教授. 主要研究方向为数据挖掘, 机器学习. 本文通信作者. E-mail: liyh@sxu.edu.cn

    任霖:山西大学计算机与信息技术学院硕士研究生. 主要研究方向为数据挖掘, 机器学习. E-mail: renlinssdx@163.com

    王素格:山西大学计算机与信息技术学院教授. 主要研究方向为自然语言处理, 机器学习. E-mail: wsg@sxu.edu.cn

    李德玉:山西大学计算机与信息技术学院教授. 主要研究方向为数据挖掘, 人工智能. E-mail: lidy@sxu.edu.cn

Online Active Learning Method for Imbalanced Data Stream

Funds: Supported by National Natural Science Foundation of China (62076158, 62072294, 41871286) and Shanxi Key Research and Development Program (201903D421041)
More Information
    Author Bio:

    LI Yan-Hong Associate professor at the School of Computer and Information Technology, Shanxi University. Her research interest covers data mining and machine learning. Corresponding author of this paper

    REN Lin Master student at the School of Computer and Information Technology, Shanxi University. His research interest covers data mining and machine learning

    WANG Su-Ge Professor at the School of Computer and Information Technology, Shanxi University. Her research interest covers natural language processing and machine learning

    LI De-Yu Professor at the School of Computer and Information Technology, Shanxi University. His research interest covers data mining and artificial intelligence

  • 摘要: 数据流分类是数据流挖掘领域一项重要研究任务, 目标是从不断变化的海量数据中捕获变化的类结构. 目前, 几乎没有框架可以同时处理数据流中常见的多类非平衡、概念漂移、异常点和标记样本成本高昂问题. 基于此, 提出一种非平衡数据流在线主动学习方法(Online active learning method for imbalanced data stream, OALM-IDS). AdaBoost是一种将多个弱分类器经过迭代生成强分类器的集成分类方法, AdaBoost.M2引入了弱分类器的置信度, 此类方法常用于静态数据. 定义了基于非平衡比率和自适应遗忘因子的训练样本重要性度量, 从而使AdaBoost.M2方法适用于非平衡数据流, 提升了非平衡数据流集成分类器的性能. 提出了边际阈值矩阵的自适应调整方法, 优化了标签请求策略. 将概念漂移程度融入模型构建过程中, 定义了基于概念漂移指数的自适应遗忘因子, 实现了漂移后的模型重构. 在6个人工数据流和4个真实数据流上的对比实验表明, 提出的非平衡数据流在线主动学习方法的分类性能优于其他5种非平衡数据流学习方法.
  • 图  1  算法框架

    Fig.  1  Algorithm framework

    图  2  6种算法的ROC曲线

    Fig.  2  ROC curve of six algorithms

    图  3  ${\rm{DS}}_{6}$的准确率曲线

    Fig.  3  Precision curve of the ${\rm{DS}}_{6}$

    图  5  Statlog的准确率曲线

    Fig.  5  Precision curve of the Statlog

    图  4  Kddcup$99\_10\%$的准确率曲线

    Fig.  4  Precision curve of the Kddcup$99\_10\%$

    图  6  消融实验结果

    Fig.  6  Result of the ablation experiment

    表  1  数据流的特征

    Table  1  Data stream feature

    数据流 样本数 特征数 类别数 类分布 漂移次数 异常点
    ${\rm{DS} }_{1}$ 200000 21 5 (0.2, 0.2, 0.2, 0.2, 0.2) 0 0
    ${\rm{DS}}_{2}$ 200000 21 5 (0.2, 0.2, 0.2, 0.2, 0.2) 3 10
    ${\rm{DS}}_{3}$ 200000 21 5 (0.1, 0.3, 0.4, 0.2, 0.1) 0 0
    ${\rm{DS}}_{4}$ 200000 21 5 (0.1, 0.3, 0.4, 0.2, 0.1) 3 10
    ${\rm{DS}}_{5}$ 200000 21 5 (0.1, 0.3, 0.4, 0.2, 0.1), (0.4, 0.2, 0.1, 0.1, 0.2) 0 0
    ${\rm{DS}}_{6}$ 200000 21 5 (0.1, 0.3, 0.4, 0.2, 0.1), (0.4, 0.2, 0.1, 0.1, 0.2) 3 10
    Kddcup$99\_10\%$ 494000 42 23
    Statlog 570000 10 7
    IoT 663000 115 11
    HAR 10299 561 6
    下载: 导出CSV

    表  2  6种算法的准确率

    Table  2  Precision values of six algorithms

    数据流 LB BOLE ${\rm{ARF}}_{RE}$ OALE CALMID OALM-IDS
    ${\rm {DS} }_{1}$ $94.56\pm0.12$ $\boldsymbol{95.61} \;\pm \boldsymbol {0.11} $ $93.54\pm0.13$ $89.78\pm0.21$ $94.76\pm0.16$ $95.48\pm0.15$
    ${\rm {DS}}_{2}$ $92.27\pm0.17$ $92.44\pm0.14$ $91.04\pm0.19$ $88.31\pm0.23$ $92.81\pm0.13$ ${\boldsymbol{93.94} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.12} }$
    ${\rm {DS}}_{3}$ $88.39\pm0.22$ $89.52\pm0.14$ $90.95\pm0.13$ $88.83\pm0.16$ $92.57\pm0.13$ ${\boldsymbol{93.72} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.13} }$
    ${\rm {DS}}_{4}$ $86.55\pm0.31$ $88.68\pm0.26$ $89.89\pm0.23$ $86.29\pm0.29$ $91.31\pm0.18$ ${\boldsymbol{92.18} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.21} }$
    ${\rm {DS}}_{5}$ $85.64\pm0.29$ $87.04\pm0.34$ $89.61\pm0.51$ $88.83\pm0.21$ $91.13\pm0.21$ ${\boldsymbol{92.92} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.16} }$
    ${\rm {DS}}_{6}$ $82.10\pm0.69$ $83.15\pm0.73$ $86.54\pm0.72$ $83.42\pm0.55$ $90.64\pm0.42$ ${\boldsymbol{92.41} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.21} }$
    Kddcup$99\_10\%$ $83.87\pm0.43$ $81.09\pm0.56$ $85.48\pm0.65$ $81.01\pm0.36$ $92.06\pm0.19$ ${\boldsymbol{92.07} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.18} }$
    Statlog $64.55\pm0.31$ $63.78\pm0.61$ $79.97\pm0.39$ $73.78\pm0.43$ $85.40\pm0.34$ ${\boldsymbol{85.68} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.33} }$
    IoT $64.03\pm0.48$ $61.54\pm0.43$ $66.66\pm0.53$ $55.81\pm0.51$ $70.85\pm0.54$ ${\boldsymbol{73.12} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.38} }$
    HAR $61.63\pm0.53$ $59.76\pm0.46$ $63.22\pm0.49$ $55.16\pm0.69$ $68.64\pm0.71$ ${\boldsymbol{69.98} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.51} }$
    下载: 导出CSV

    表  3  6种算法的召回率

    Table  3  Recall values of six algorithms

    数据流 LB BOLE ${\rm{ARF}}_{RE}$ OALE CALMID OALM-IDS
    ${\rm{DS}}_{1}$ $95.37\pm0.18$ $95.96\pm0.13$ $93.39\pm0.11$ $90.13\pm0.13$ $95.91\pm0.11$ ${\boldsymbol{96.14} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.12} }$
    ${\rm{DS}}_{2}$ $92.39\pm0.21$ $92.28\pm0.35$ $91.35\pm0.26$ $89.45\pm0.18$ $92.51\pm0.15$ ${\boldsymbol{94.08} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.14} }$
    ${\rm{DS}}_{3}$ $87.55\pm0.19$ $88.19\pm0.22$ $86.14\pm0.21$ $88.52\pm0.22$ $90.55\pm0.13$ ${\boldsymbol{92.52} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.13} }$
    ${\rm{DS}}_{4}$ $84.57\pm0.36$ $86.73\pm0.29$ $87.47\pm0.28$ $83.05\pm0.31$ $89.89\pm0.21$ ${\boldsymbol{92.44} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.18} }$
    ${\rm{DS}}_{5}$ $84.14\pm0.43$ $86.44\pm0.49$ $87.26\pm0.69$ $83.26\pm0.36$ $90.25\pm0.18$ ${\boldsymbol{91.16} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.13} }$
    ${\rm{DS}}_{6}$ $83.98\pm1.13$ $81.87\pm0.91$ $84.56\pm1.31$ $78.87\pm0.69$ $90.46\pm0.13$ ${\boldsymbol{90.71} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.21} }$
    Kddcup$99\_10\%$ $60.82\pm0.71$ $62.75\pm0.64$ $58.17\pm1.32$ $58.44\pm1.63$ $61.88\pm0.43$ ${\boldsymbol{63.71} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.37} }$
    Statlog $61.39\pm0.91$ $50.92\pm1.32$ $54.36\pm1.11$ $51.20\pm1.34$ $59.52\pm0.63$ ${\boldsymbol{63.12} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.39} }$
    IoT $40.73\pm2.14$ $42.29\pm1.58$ $39.35\pm1.89$ $40.42\pm2.15$ $48.04\pm1.04$ ${\boldsymbol{51.26} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.81} }$
    HAR $61.64\pm1.18$ $60.57\pm0.97$ $57.91\pm1.43$ $54.11\pm1.36$ $65.53\pm0.76$ ${\boldsymbol{66.57} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.46} }$
    下载: 导出CSV

    表  4  6种算法的F1值

    Table  4  F1 values of six algorithms

    数据流 LB BOLE ${\rm{ARF}}_{RE}$ OALE CALMID OALM-IDS
    ${\rm{DS}}_{1}$ $94.96\pm0.11$ ${\boldsymbol{95.80} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.10} }$ $93.42\pm0.13$ $89.93\pm0.15$ $95.33\pm0.11$ ${\boldsymbol{95.80} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.10} }$
    ${\rm{DS}}_{2}$ $92.32\pm0.16$ $92.34\pm0.13$ $91.18\pm0.15$ $88.85\pm0.21$ $92.65\pm0.13$ ${\boldsymbol{94.01} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.12} }$
    ${\rm{DS}}_{3}$ $87.91\pm0.20$ $88.81\pm0.24$ $88.11\pm0.36$ $88.67\pm0.20$ $91.50\pm0.16$ ${\boldsymbol{93.07} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.14} }$
    ${\rm{DS}}_{4}$ $85.35\pm0.42$ $87.38\pm0.36$ $88.42\pm0.51$ $84.50\pm0.33$ $90.51\pm0.21$ ${\boldsymbol{92.29} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.20} }$
    ${\rm{DS}}_{5}$ $84.85\pm0.41$ $86.67\pm0.43$ $88.30\pm0.46$ $85.36\pm0.48$ $90.62\pm0.21$ ${\boldsymbol{91.93} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.18} }$
    ${\rm{DS}}_{6}$ $82.97\pm0.87$ $82.43\pm0.71$ $85.35\pm0.91$ $80.59\pm0.63$ $90.46\pm0.39$ ${\boldsymbol{91.53} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.31} }$
    Kddcup$99\_10\%$ $73.12\pm0.55$ $72.47\pm0.63$ $72.01\pm0.46$ $72.81\pm0.51$ $73.56\pm0.33$ ${\boldsymbol{74.65} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.20} }$
    Statlog $66.18\pm0.83$ $54.32\pm1.91$ $63.85\pm1.03$ $63.42\pm0.98$ $74.42\pm0.36$ ${\boldsymbol{75.19} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.31} }$
    IoT $47.01\pm1.24$ $48.40\pm0.96$ $47.34\pm1.89$ $44.94\pm1.36$ $54.26\pm0.65$ ${\boldsymbol{56.73} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.67} }$
    HAR $59.93\pm0.91$ $58.81\pm1.21$ $58.52\pm0.79$ $54.43\pm1.13$ $65.43\pm0.63$ ${\boldsymbol{67.76} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.58} }$
    下载: 导出CSV

    表  5  6种算法的Kappa系数值

    Table  5  Kappa coefficient values of six algorithms

    数据流 LB BOLE ${\rm{ARF}}_{RE}$ OALE CALMID OALM-IDS
    ${\rm{DS}}_{1}$ $90.17\pm0.12$ $91.18\pm0.14$ $90.59\pm0.16$ $85.47\pm0.21$ $90.48\pm0.19$ $\boldsymbol{91.31\pm0.12}$
    ${\rm{DS}}_{2}$ $88.85\pm0.19$ $88.14\pm0.23$ $87.91\pm0.39$ $83.18\pm0.56$ $89.97\pm0.31$ ${\boldsymbol{90.66} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.23} }$
    ${\rm{DS}}_{3}$ $85.25\pm0.22$ $85.86\pm0.38$ $86.68\pm0.29$ $83.91\pm0.39$ $88.91\pm0.26$ ${\boldsymbol{89.93} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.21} }$
    ${\rm{DS}}_{4}$ $84.15\pm0.55$ $86.04\pm0.63$ $87.14\pm0.66$ $83.42\pm0.71$ $88.92\pm0.33$ ${\boldsymbol{89.33} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.36} }$
    ${\rm{DS} }_{5}$ $83.85\pm0.77$ $85.83\pm0.69$ $86.45\pm0.81$ $86.67\pm0.70$ $88.57\pm0.31$ ${\boldsymbol{89.12} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.29} }$
    ${\rm{DS} }_{6} $ $81.49\pm1.12$ $82.98\pm1.69$ $84.15\pm1.87$ $79.92\pm1.48$ $89.01\pm0.41$ ${\boldsymbol{89.73} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.28} }$
    Kddcup$99\_10\% $ $80.93\pm0.67$ $75.62\pm1.13$ $79.32\pm1.32$ $78.31\pm0.91$ $83.32\pm0.26$ ${\boldsymbol{85.83} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.18} }$
    Statlog $58.71\pm1.42$ $61.43\pm1.18$ $73.72\pm0.93$ $71.21\pm1.24$ $79.39\pm0.46$ ${\boldsymbol{80.11} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.19} }$
    IoT $67.53\pm1.54$ $65.02\pm1.89$ $68.99\pm2.14$ $59.53\pm2.12$ $71.65\pm0.71$ ${\boldsymbol{73.29} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.68} }$
    HAR $60.49\pm1.12$ $60.01\pm1.38$ $61.86\pm1.13$ $56.75\pm2.03$ $68.52\pm0.76$ ${\boldsymbol{69.64} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.71} }$
    下载: 导出CSV

    表  6  参数$\theta $对OALM-IDS的影响

    Table  6  Effect of parameter $\theta $ to OALM-IDS

    数据流 $\theta $ $b$ 准确率 召回率 F1值 Kappa系数值
    0.4 0.17143 $94.21\pm0.16 $ $93.18\pm0.12$ $94.13\pm0.11$ $90.11\pm0.12$
    $\rm{DS}_{1}$ ${\boldsymbol{0.5}}$ ${\boldsymbol{0.180\,26} }$ ${\boldsymbol{95.48} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.15} }$ ${\boldsymbol{96.14}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.12}}$ ${\boldsymbol{95.80}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.10}}$ ${\boldsymbol{91.31}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.12}}$
    0.6 0.19782 $95.03\pm0.15 $ $93.19\pm0.12$ $95.16\pm0.10$ $91.01\pm0.12$
    0.4 0.17136 $93.01\pm0.12 $ $92.81\pm0.16$ $93.04\pm0.13$ $89.09\pm0.26$
    $\rm{DS}_{2}$ ${\boldsymbol{0.5}}$ ${\boldsymbol{0.191\,78} }$ ${\boldsymbol{93.94}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.12}}$ ${\boldsymbol{94.08}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.14}}$ ${\boldsymbol{94.01}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.12}}$ ${\boldsymbol{90.66}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.23}}$
    0.6 0.20000 $93.18\pm0.13 $ $93.16\pm0.14$ $93.75\pm0.12$ $90.07\pm0.23$
    0.4 0.17821 $93.24\pm0.13 $ $92.05\pm0.13$ $92.54\pm0.16$ $88.56\pm0.22$
    $\rm{DS}_{3}$ ${\boldsymbol{0.5}}$ ${\boldsymbol{0.195\,12} }$ ${\boldsymbol{93.72}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.13}}$ ${\boldsymbol{92.52}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.13}}$ ${\boldsymbol{93.07}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.14}}$ ${\boldsymbol{89.93}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.21}}$
    0.6 0.20000 $93.43\pm0.13 $ $92.24\pm0.13$ $92.10\pm0.14$ $88.71\pm0.21$
    0.4 0.18423 $91.63\pm0.21 $ $91.34\pm0.18$ $91.76\pm0.20$ $88.54\pm0.38$
    $\rm{DS}_{4}$ ${\boldsymbol{0.5}}$ ${\boldsymbol{0.198\,77} }$ ${\boldsymbol{92.18}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.21}}$ ${\boldsymbol{92.44}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.18}}$ ${\boldsymbol{92.29}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.20}}$ ${\boldsymbol{89.33}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.36}}$
    0.6 0.20000 $91.06\pm0.21 $ $91.56\pm0.19$ $91.80\pm0.21$ $88.63\pm0.36$
    0.4 0.18002 $92.01\pm0.16 $ $90.46\pm0.13$ $90.76\pm0.18$ $88.42\pm0.29$
    $\rm{DS}_{5}$ ${\boldsymbol{0.5}}$ ${\boldsymbol{0.197\,22} }$ ${\boldsymbol{92.92} }\;{\boldsymbol{\pm}}\;{\boldsymbol{0.16} }$ ${\boldsymbol{91.16}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.13}}$ ${\boldsymbol{91.93}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.18}}$ ${\boldsymbol{89.12}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.29}}$
    0.6 0.20000 $92.50\pm0.16 $ $90.76\pm0.13$ $91.21\pm0.19$ $88.56\pm0.30$
    0.4 0.18331 $91.02\pm0.21 $ $89.03\pm0.22$ $90.32\pm0.31$ $88.12\pm0.28$
    $\rm{DS}_{6}$ ${\boldsymbol{0.5}}$ ${\boldsymbol{0.199\,23} }$ ${\boldsymbol{92.41}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.21}}$ ${\boldsymbol{90.71}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.21}}$ ${\boldsymbol{91.53}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.31}}$ ${\boldsymbol{89.73}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.28}}$
    0.6 0.20000 $91.01\pm0.21 $ $89.92\pm0.22$ $90.12\pm0.31$ $89.13\pm0.28$
    0.4 0.18188 $90.59\pm0.18 $ $63.51\pm0.37$ $73.35\pm0.20$ $83.14\pm0.18$
    Kddcup$99\_10\%$ ${\boldsymbol{0.5}}$ ${\boldsymbol{0.199\,61} }$ ${\boldsymbol{92.07}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.18}}$ ${\boldsymbol{63.71}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.37}}$ ${\boldsymbol{74.65}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.20}}$ ${\boldsymbol{85.83}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.18}}$
    0.6 0.20000 $91.63\pm0.18 $ $63.63\pm0.37$ $74.43\pm0.21$ $85.61\pm0.18$
    0.4 0.19022 $84.75\pm0.33 $ $62.19\pm0.39$ $74.85\pm0.31$ $78.86\pm0.19$
    Statlog ${\boldsymbol{0.5}}$ ${\boldsymbol{0.199\,94} }$ ${\boldsymbol{85.68}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.33}}$ ${\boldsymbol{63.12}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.39}}$ ${\boldsymbol{75.19}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.31}}$ ${\boldsymbol{80.11}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.19}}$
    0.6 0.20000 $85.66\pm0.33 $ $63.01\pm0.39$ $75.19\pm0.31$ $79.89\pm0.19$
    0.4 0.19113 $71.21\pm0.38 $ $49.86\pm0.81$ $51.21\pm0.67$ $71.61\pm0.68$
    IoT ${\boldsymbol{0.5}}$ ${\boldsymbol{0.196\,84} }$ ${\boldsymbol{73.12}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.38}}$ ${\boldsymbol{51.26}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.81}}$ ${\boldsymbol{56.73}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.67}}$ ${\boldsymbol{73.29}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.68}}$
    0.6 0.20000 $72.11\pm0.39 $ $50.06\pm0.81$ $54.33\pm0.67$ $71.34\pm0.68$
    0.4 0.18634 $66.54\pm0.52 $ $64.32\pm0.48$ $65.05\pm0.59$ $66.81\pm0.72$
    HAR ${\boldsymbol{0.5}}$ ${\boldsymbol{0.195\,47} }$ ${\boldsymbol{69.98}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.51}}$ ${\boldsymbol{66.57}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.46}}$ ${\boldsymbol{67.76}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.58}}$ ${\boldsymbol{69.64}}\;{\boldsymbol{\pm}}\;{\boldsymbol{0.71}}$
    0.6 0.20000 $64.32\pm0.52 $ $65.14\pm0.46$ $66.11\pm0.58$ $64.32\pm0.71$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-29
  • 录用日期:  2022-04-07
  • 网络出版日期:  2024-06-19
  • 刊出日期:  2024-07-23

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