Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (4): 1137-1147.DOI: 10.11772/j.issn.1001-9081.2021071259
Special Issue: CCF第36届中国计算机应用大会 (CCF NCCA 2021)
• The 36 CCF National Conference of Computer Applications (CCF NCCA 2020) • Previous Articles Next Articles
Le WANG, Meng HAN(), Xiaojuan LI, Ni ZHANG, Haodong CHENG
Received:
2021-07-16
Revised:
2021-08-16
Accepted:
2021-08-25
Online:
2021-08-16
Published:
2022-04-10
Contact:
Meng HAN
About author:
WANG Le, born in 1994, M. S. candidate. Her research interests include data mining, data stream ensemble classification.Supported by:
通讯作者:
韩萌
作者简介:
王乐(1994—),女,吉林白城人,硕士研究生,CCF会员,主要研究方向:数据挖掘、数据流集成分类基金资助:
CLC Number:
Le WANG, Meng HAN, Xiaojuan LI, Ni ZHANG, Haodong CHENG. Ensemble classification algorithm based on dynamic weighting function[J]. Journal of Computer Applications, 2022, 42(4): 1137-1147.
王乐, 韩萌, 李小娟, 张妮, 程浩东. 基于动态加权函数的集成分类算法[J]. 《计算机应用》唯一官方网站, 2022, 42(4): 1137-1147.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021071259
数据块 | 样本号 | 样本 | 类别 | 数据块 | 样本号 | 样本 | 类别 |
---|---|---|---|---|---|---|---|
B1 | 1 | (1.5, 2) | + | B3 | 1 | (1, 5.6) | + |
2 | (4.3, 5) | + | 2 | (3, 6) | + | ||
3 | (2, 3) | - | 3 | (5, 1) | + | ||
4 | (5, 2) | - | 4 | (3.9, 2) | - | ||
5 | (5.2, 4) | - | 5 | (5, 5) | - | ||
6 | (6, 1) | - | 6 | (2, 1) | - | ||
B2 | 1 | (2, 3) | + | ||||
2 | (6, 2) | + | |||||
3 | (6, 6.3) | - | |||||
4 | (5.1, 4) | - | |||||
5 | (5, 7) | - | |||||
6 | (1.7, 4) | - |
Tab. 1 Data block instance samples
数据块 | 样本号 | 样本 | 类别 | 数据块 | 样本号 | 样本 | 类别 |
---|---|---|---|---|---|---|---|
B1 | 1 | (1.5, 2) | + | B3 | 1 | (1, 5.6) | + |
2 | (4.3, 5) | + | 2 | (3, 6) | + | ||
3 | (2, 3) | - | 3 | (5, 1) | + | ||
4 | (5, 2) | - | 4 | (3.9, 2) | - | ||
5 | (5.2, 4) | - | 5 | (5, 5) | - | ||
6 | (6, 1) | - | 6 | (2, 1) | - | ||
B2 | 1 | (2, 3) | + | ||||
2 | (6, 2) | + | |||||
3 | (6, 6.3) | - | |||||
4 | (5.1, 4) | - | |||||
5 | (5, 7) | - | |||||
6 | (1.7, 4) | - |
训练次数 | 分类器 | 权重 | ε |
---|---|---|---|
第一次训练(B1) | 2.900 | ||
第二次训练(B2) | 2.900 | ||
2.265 | |||
第三次训练(B3) | 2.340 | ||
2.050 | |||
2.625 |
Tab. 2 Classifier training process
训练次数 | 分类器 | 权重 | ε |
---|---|---|---|
第一次训练(B1) | 2.900 | ||
第二次训练(B2) | 2.900 | ||
2.265 | |||
第三次训练(B3) | 2.340 | ||
2.050 | |||
2.625 |
数据流 | function | InstanceRandomSeed | peturbFraction |
---|---|---|---|
Agrawal1 | 1 | 1 | 0.05 |
Agrawal2 | 1 | 1 | 0.20 |
Agrawal3 | 3 | 1 | 0.05 |
Tab. 3 Parameters of Agrawal data streams
数据流 | function | InstanceRandomSeed | peturbFraction |
---|---|---|---|
Agrawal1 | 1 | 1 | 0.05 |
Agrawal2 | 1 | 1 | 0.20 |
Agrawal3 | 3 | 1 | 0.05 |
数据流 | NumDriftAtts | NoisePercentage |
---|---|---|
Hyperplane1 | 2 | 5 |
Hyperplane2 | 2 | 10 |
Hyperplane3 | 10 | 10 |
Tab. 4 Parameters of Hyperplane data streams
数据流 | NumDriftAtts | NoisePercentage |
---|---|---|
Hyperplane1 | 2 | 5 |
Hyperplane2 | 2 | 10 |
Hyperplane3 | 10 | 10 |
数据集 | 准确率/% | ||||
---|---|---|---|---|---|
d=500 | d=800 | d=1 000 | d=1 500 | d=2 000 | |
Hyperplane1 | 91.31 | 91.33 | 91.19 | 91.35 | 91.20 |
Hyperplane2 | 86.53 | 86.57 | 86.42 | 86.63 | 86.54 |
Hyperplane3 | 86.62 | 86.56 | 86.39 | 86.52 | 86.52 |
Agrawal1 | 95.02 | 94.99 | 95.01 | 95.01 | 95.00 |
Agrawal2 | 90.11 | 90.06 | 90.07 | 90.06 | 90.08 |
Agrawal3 | 97.54 | 97.53 | 97.52 | 97.53 | 97.50 |
SEA | 89.91 | 89.91 | 89.90 | 89.90 | 89.91 |
Waveform | 85.95 | 85.92 | 85.96 | 85.98 | 85.94 |
LED | 74.04 | 74.02 | 74.04 | 74.02 | 74.07 |
RBF | 95.51 | 95.52 | 95.57 | 95.55 | 95.55 |
Tab. 5 EDW accuracies of different block sizes on each dataset
数据集 | 准确率/% | ||||
---|---|---|---|---|---|
d=500 | d=800 | d=1 000 | d=1 500 | d=2 000 | |
Hyperplane1 | 91.31 | 91.33 | 91.19 | 91.35 | 91.20 |
Hyperplane2 | 86.53 | 86.57 | 86.42 | 86.63 | 86.54 |
Hyperplane3 | 86.62 | 86.56 | 86.39 | 86.52 | 86.52 |
Agrawal1 | 95.02 | 94.99 | 95.01 | 95.01 | 95.00 |
Agrawal2 | 90.11 | 90.06 | 90.07 | 90.06 | 90.08 |
Agrawal3 | 97.54 | 97.53 | 97.52 | 97.53 | 97.50 |
SEA | 89.91 | 89.91 | 89.90 | 89.90 | 89.91 |
Waveform | 85.95 | 85.92 | 85.96 | 85.98 | 85.94 |
LED | 74.04 | 74.02 | 74.04 | 74.02 | 74.07 |
RBF | 95.51 | 95.52 | 95.57 | 95.55 | 95.55 |
算法 | Hyperplane1 | Hyperplane2 | Hyperplane3 | ||||||
---|---|---|---|---|---|---|---|---|---|
nodes | leaves | depth | nodes | leaves | depth | nodes | leaves | depth | |
EDW | 119.00 | 60.00 | 6.30 | 118.00 | 59.50 | 6.30 | 118.20 | 59.60 | 6.30 |
AUE2 | 1 887.00 | 944.00 | 14.40 | 1 821.60 | 911.30 | 11.90 | 1 785.00 | 893.00 | 12.00 |
LeveragingBag | 5 168.00 | 2 584.50 | 18.30 | 822.80 | 411.90 | 11.40 | 4 991.20 | 2 496.10 | 17.60 |
OzaBoost | 697.80 | 349.40 | 12.20 | 131.20 | 66.10 | 8.70 | 676.40 | 338.70 | 12.20 |
OzaBoostAdwin | 21 641.00 | 10 821.00 | 15.10 | 22 326.80 | 11 163.90 | 13.40 | 29 134.40 | 14 567.70 | 17.30 |
OzaBag | 686.80 | 343.90 | 11.20 | 133.20 | 67.10 | 7.20 | 676.80 | 338.90 | 10.80 |
BOLE | 474.20 | 237.60 | 10.50 | 32.60 | 16.80 | 2.10 | 464.40 | 232.70 | 9.80 |
LimAttClassifier | 7 391.20 | 3 696.10 | 24.60 | 7 402.20 | 3 701.60 | 24.30 | 7 305.80 | 3 653.40 | 23.40 |
ARF | 7 347.00 | 3 674.00 | 27.00 | 343.00 | 3 672.00 | 21.00 | 7 345.00 | 3 673.00 | 20.00 |
算法 | Agrawal1 | Agrawal2 | Agrawal3 | ||||||
nodes | leaves | depth | nodes | leaves | depth | nodes | leaves | depth | |
EDW | 122.30 | 88.60 | 6.40 | 153.70 | 130.80 | 6.70 | 92.10 | 65.00 | 4.50 |
AUE2 | 840.20 | 589.80 | 9.20 | 1 017.50 | 773.00 | 8.90 | 528.80 | 440.40 | 6.60 |
LeveragingBag | 301.30 | 233.30 | 7.80 | 3 487.00 | 2 403.70 | 12.60 | 324.20 | 278.10 | 6.30 |
OzaBoost | 267.20 | 210.90 | 7.20 | 1 158.80 | 848.30 | 9.50 | 696.30 | 596.50 | 7.60 |
OzaBoostAdwin | 13 030.00 | 7 644.50 | 8.50 | 34 775.10 | 20 655.60 | 11.20 | 2 071.70 | 1 344.60 | 7.00 |
OzaBag | 116.20 | 90.10 | 6.70 | 403.90 | 324.10 | 10.40 | 117.20 | 85.80 | 5.40 |
BOLE | 101.10 | 78.20 | 4.00 | 247.30 | 177.30 | 5.40 | 545.10 | 425.00 | 5.80 |
LimAttClassifier | 2 138.89 | 1 128.44 | 16.00 | 2 763.22 | 1 692.00 | 30.89 | 2 251.67 | 1 189.78 | 17.22 |
ARF | 5 217.00 | 3 895.00 | 18.00 | 4 848.00 | 3 570.00 | 14.00 | 4 623.00 | 3 424.00 | 16.00 |
Tab. 6 Spanning tree scale comparison
算法 | Hyperplane1 | Hyperplane2 | Hyperplane3 | ||||||
---|---|---|---|---|---|---|---|---|---|
nodes | leaves | depth | nodes | leaves | depth | nodes | leaves | depth | |
EDW | 119.00 | 60.00 | 6.30 | 118.00 | 59.50 | 6.30 | 118.20 | 59.60 | 6.30 |
AUE2 | 1 887.00 | 944.00 | 14.40 | 1 821.60 | 911.30 | 11.90 | 1 785.00 | 893.00 | 12.00 |
LeveragingBag | 5 168.00 | 2 584.50 | 18.30 | 822.80 | 411.90 | 11.40 | 4 991.20 | 2 496.10 | 17.60 |
OzaBoost | 697.80 | 349.40 | 12.20 | 131.20 | 66.10 | 8.70 | 676.40 | 338.70 | 12.20 |
OzaBoostAdwin | 21 641.00 | 10 821.00 | 15.10 | 22 326.80 | 11 163.90 | 13.40 | 29 134.40 | 14 567.70 | 17.30 |
OzaBag | 686.80 | 343.90 | 11.20 | 133.20 | 67.10 | 7.20 | 676.80 | 338.90 | 10.80 |
BOLE | 474.20 | 237.60 | 10.50 | 32.60 | 16.80 | 2.10 | 464.40 | 232.70 | 9.80 |
LimAttClassifier | 7 391.20 | 3 696.10 | 24.60 | 7 402.20 | 3 701.60 | 24.30 | 7 305.80 | 3 653.40 | 23.40 |
ARF | 7 347.00 | 3 674.00 | 27.00 | 343.00 | 3 672.00 | 21.00 | 7 345.00 | 3 673.00 | 20.00 |
算法 | Agrawal1 | Agrawal2 | Agrawal3 | ||||||
nodes | leaves | depth | nodes | leaves | depth | nodes | leaves | depth | |
EDW | 122.30 | 88.60 | 6.40 | 153.70 | 130.80 | 6.70 | 92.10 | 65.00 | 4.50 |
AUE2 | 840.20 | 589.80 | 9.20 | 1 017.50 | 773.00 | 8.90 | 528.80 | 440.40 | 6.60 |
LeveragingBag | 301.30 | 233.30 | 7.80 | 3 487.00 | 2 403.70 | 12.60 | 324.20 | 278.10 | 6.30 |
OzaBoost | 267.20 | 210.90 | 7.20 | 1 158.80 | 848.30 | 9.50 | 696.30 | 596.50 | 7.60 |
OzaBoostAdwin | 13 030.00 | 7 644.50 | 8.50 | 34 775.10 | 20 655.60 | 11.20 | 2 071.70 | 1 344.60 | 7.00 |
OzaBag | 116.20 | 90.10 | 6.70 | 403.90 | 324.10 | 10.40 | 117.20 | 85.80 | 5.40 |
BOLE | 101.10 | 78.20 | 4.00 | 247.30 | 177.30 | 5.40 | 545.10 | 425.00 | 5.80 |
LimAttClassifier | 2 138.89 | 1 128.44 | 16.00 | 2 763.22 | 1 692.00 | 30.89 | 2 251.67 | 1 189.78 | 17.22 |
ARF | 5 217.00 | 3 895.00 | 18.00 | 4 848.00 | 3 570.00 | 14.00 | 4 623.00 | 3 424.00 | 16.00 |
算法 | 准确率 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Hyperplane1 | Hyperplane2 | Hyperplane3 | Agrawal1 | Agrawal2 | Agrawal3 | SEA | RBF | Waveform | LED | |
EDW | 91.36 | 86.56 | 86.56 | 95.04 | 90.12 | 97.54 | 89.91 | 95.53 | 85.94 | 74.01 |
AUE2 | 91.26 | 86.54 | 86.61 | 95.03 | 90.10 | 97.54 | 89.47 | 95.47 | 85.35 | 73.99 |
LeveragingBag | 91.33 | 86.38 | 86.51 | 95.02 | 90.10 | 97.50 | 89.88 | 95.03 | 85.09 | 74.07 |
OzaBoost | 90.28 | 86.57 | 85.39 | 93.42 | 89.23 | 96.67 | 89.38 | 94.57 | 84.73 | 74.01 |
OzaBoostAdwin | 83.96 | 76.94 | 77.31 | 93.42 | 88.09 | 96.87 | 86.88 | 94.34 | 80.75 | 73.11 |
OzaBag | 91.03 | 86.67 | 86.31 | 94.81 | 90.08 | 97.52 | 89.74 | 95.00 | 85.46 | 74.04 |
BOLE | 89.13 | 86.68 | 83.13 | 92.80 | 87.58 | 96.87 | 89.62 | 94.37 | 82.27 | 74.0 |
LimAttClassifier | 77.33 | 73.10 | 74.02 | 94.31 | 88.94 | 66.65 | 78.86 | 72.01 | 82.16 | 74.03 |
ADOB | 49.92 | 79.24 | 64.00 | 94.53 | 87.47 | 96.76 | 87.60 | 50.15 | 33.38 | 10.06 |
ARF | 76.43 | 73.62 | 73.65 | 74.58 | 71.61 | 71.35 | 87.55 | 90.94 | 75.64 | 28.47 |
Tab. 7 Accuracy comparison of different algorithms
算法 | 准确率 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Hyperplane1 | Hyperplane2 | Hyperplane3 | Agrawal1 | Agrawal2 | Agrawal3 | SEA | RBF | Waveform | LED | |
EDW | 91.36 | 86.56 | 86.56 | 95.04 | 90.12 | 97.54 | 89.91 | 95.53 | 85.94 | 74.01 |
AUE2 | 91.26 | 86.54 | 86.61 | 95.03 | 90.10 | 97.54 | 89.47 | 95.47 | 85.35 | 73.99 |
LeveragingBag | 91.33 | 86.38 | 86.51 | 95.02 | 90.10 | 97.50 | 89.88 | 95.03 | 85.09 | 74.07 |
OzaBoost | 90.28 | 86.57 | 85.39 | 93.42 | 89.23 | 96.67 | 89.38 | 94.57 | 84.73 | 74.01 |
OzaBoostAdwin | 83.96 | 76.94 | 77.31 | 93.42 | 88.09 | 96.87 | 86.88 | 94.34 | 80.75 | 73.11 |
OzaBag | 91.03 | 86.67 | 86.31 | 94.81 | 90.08 | 97.52 | 89.74 | 95.00 | 85.46 | 74.04 |
BOLE | 89.13 | 86.68 | 83.13 | 92.80 | 87.58 | 96.87 | 89.62 | 94.37 | 82.27 | 74.0 |
LimAttClassifier | 77.33 | 73.10 | 74.02 | 94.31 | 88.94 | 66.65 | 78.86 | 72.01 | 82.16 | 74.03 |
ADOB | 49.92 | 79.24 | 64.00 | 94.53 | 87.47 | 96.76 | 87.60 | 50.15 | 33.38 | 10.06 |
ARF | 76.43 | 73.62 | 73.65 | 74.58 | 71.61 | 71.35 | 87.55 | 90.94 | 75.64 | 28.47 |
算法 | 消耗时间 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Hyperplane1 | Hyperplane2 | Hyperplane3 | Agrawal1 | Agrawal2 | Agrawal3 | SEA | RBF | Waveform | LED | |
EDW | 57.51 | 56.51 | 57.43 | 30.74 | 31.17 | 27.07 | 19.49 | 51.89 | 129.00 | 137.00 |
AUE2 | 79.00 | 76.00 | 70.00 | 32.40 | 45.42 | 31.46 | 29.01 | 67.00 | 136.00 | 114.00 |
LeveragingBag | 132.00 | 68.00 | 122.00 | 30.09 | 79.00 | 28.46 | 29.62 | 97.00 | 115.00 | 103.00 |
OzaBoost | 47.01 | 29.81 | 45.88 | 21.39 | 35.48 | 23.68 | 15.31 | 45.33 | 68.00 | 72.00 |
OzaBoostAdwin | 1 016.00 | 1 980.00 | 1 636.00 | 123.00 | 304.00 | 41.81 | 2 089.00 | 260.00 | 365.00 | 93.00 |
OzaBag | 43.25 | 27.12 | 44.12 | 15.32 | 21.34 | 14.93 | 12.40 | 42.51 | 57.24 | 50.91 |
BOLE | 61.00 | 61.00 | 58.60 | 48.44 | 57.48 | 33.80 | 15.00 | 55.61 | 135.00 | 87.00 |
LimAttClassifier | 54.05 | 52.47 | 54.04 | 26.30 | 34.84 | 27.93 | 14.40 | 55.26 | 127.00 | 73.00 |
ADOB | 2 722.00 | 1 711.00 | 1 264.00 | 1 413.00 | 1 545.00 | 1 376.00 | 645.00 | 1 970.00 | 5 502.00 | 152.00 |
ARF | 13.14 | 14.21 | 14.53 | 7.38 | 7.08 | 6.52 | 6.17 | 15.30 | 26.33 | 62.00 |
Tab. 8 Time cost comparison of different algorithms
算法 | 消耗时间 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Hyperplane1 | Hyperplane2 | Hyperplane3 | Agrawal1 | Agrawal2 | Agrawal3 | SEA | RBF | Waveform | LED | |
EDW | 57.51 | 56.51 | 57.43 | 30.74 | 31.17 | 27.07 | 19.49 | 51.89 | 129.00 | 137.00 |
AUE2 | 79.00 | 76.00 | 70.00 | 32.40 | 45.42 | 31.46 | 29.01 | 67.00 | 136.00 | 114.00 |
LeveragingBag | 132.00 | 68.00 | 122.00 | 30.09 | 79.00 | 28.46 | 29.62 | 97.00 | 115.00 | 103.00 |
OzaBoost | 47.01 | 29.81 | 45.88 | 21.39 | 35.48 | 23.68 | 15.31 | 45.33 | 68.00 | 72.00 |
OzaBoostAdwin | 1 016.00 | 1 980.00 | 1 636.00 | 123.00 | 304.00 | 41.81 | 2 089.00 | 260.00 | 365.00 | 93.00 |
OzaBag | 43.25 | 27.12 | 44.12 | 15.32 | 21.34 | 14.93 | 12.40 | 42.51 | 57.24 | 50.91 |
BOLE | 61.00 | 61.00 | 58.60 | 48.44 | 57.48 | 33.80 | 15.00 | 55.61 | 135.00 | 87.00 |
LimAttClassifier | 54.05 | 52.47 | 54.04 | 26.30 | 34.84 | 27.93 | 14.40 | 55.26 | 127.00 | 73.00 |
ADOB | 2 722.00 | 1 711.00 | 1 264.00 | 1 413.00 | 1 545.00 | 1 376.00 | 645.00 | 1 970.00 | 5 502.00 | 152.00 |
ARF | 13.14 | 14.21 | 14.53 | 7.38 | 7.08 | 6.52 | 6.17 | 15.30 | 26.33 | 62.00 |
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