Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (8): 2466-2475.DOI: 10.11772/j.issn.1001-9081.2023081145
• Data science and technology • Previous Articles Next Articles
Qiangkui LENG(), Xuezi SUN, Xiangfu MENG
Received:
2023-08-29
Revised:
2023-10-22
Accepted:
2023-11-03
Online:
2024-08-22
Published:
2024-08-10
Contact:
Qiangkui LENG
About author:
LENG Qiangkui , born in 1981, Ph. D., professor. His researchinterests include artificial intelligence, machine learning.Supported by:
通讯作者:
冷强奎
作者简介:
冷强奎(1981—),男,辽宁建平人,教授,博士生导师,博士,CCF高级会员,主要研究方向:人工智能、机器学习 lqk_306@163.com基金资助:
CLC Number:
Qiangkui LENG, Xuezi SUN, Xiangfu MENG. Oversampling method for imbalanced data based on sample potential and noise evolution[J]. Journal of Computer Applications, 2024, 44(8): 2466-2475.
冷强奎, 孙薛梓, 孟祥福. 基于样本势和噪声进化的不平衡数据过采样方法[J]. 《计算机应用》唯一官方网站, 2024, 44(8): 2466-2475.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023081145
数据集名称 | 不平衡 率 | 属性 数 | 样本数 | 少数类 样本数 | 多数类 样本数 | 缩写 |
---|---|---|---|---|---|---|
EEG EYE | 1.23 | 14 | 14 980 | 6 723 | 8 257 | D1 |
ecoli-0_vs_1 | 1.86 | 7 | 220 | 77 | 143 | D2 |
glass0 | 2.06 | 9 | 214 | 70 | 144 | D3 |
LanSat | 2.12 | 36 | 6 435 | 2 061 | 4 374 | D4 |
Abalone | 2.20 | 8 | 4 177 | 1 307 | 2 870 | D5 |
yeast1 | 2.46 | 8 | 1 484 | 429 | 1 055 | D6 |
glass-0-1-2-3_vs_4-5-6 | 3.20 | 9 | 214 | 51 | 163 | D7 |
ecoli1 | 3.36 | 7 | 336 | 77 | 259 | D8 |
new-thyroid2 | 5.14 | 5 | 215 | 35 | 180 | D9 |
ecoli2 | 5.46 | 7 | 336 | 52 | 284 | D10 |
sgment0 | 6.02 | 19 | 2 308 | 329 | 1 979 | D11 |
yeast3 | 8.10 | 8 | 1 484 | 163 | 1 321 | D12 |
ecoli3 | 8.60 | 7 | 336 | 35 | 301 | D13 |
yeast-2_vs_4 | 9.08 | 8 | 514 | 51 | 463 | D14 |
Pendigits | 9.42 | 16 | 10 992 | 1 055 | 9 937 | D15 |
vowel0 | 9.98 | 13 | 988 | 90 | 898 | D16 |
MNIST384 | 10.00 | 784 | 15 544 | 1 413 | 14 131 | D17 |
shuttle-c0-vs-c4 | 13.87 | 9 | 1 829 | 123 | 1 706 | D18 |
glass-0-1-6_vs_5 | 19.44 | 9 | 184 | 9 | 175 | D19 |
shuttle-c2-vs-c4 | 20.50 | 9 | 129 | 6 | 123 | D20 |
yeast5 | 32.73 | 8 | 1 484 | 44 | 1 440 | D21 |
ecoli-0-1-3-7_vs_2-6 | 39.14 | 7 | 281 | 7 | 274 | D22 |
Tab.1 Description of datasets
数据集名称 | 不平衡 率 | 属性 数 | 样本数 | 少数类 样本数 | 多数类 样本数 | 缩写 |
---|---|---|---|---|---|---|
EEG EYE | 1.23 | 14 | 14 980 | 6 723 | 8 257 | D1 |
ecoli-0_vs_1 | 1.86 | 7 | 220 | 77 | 143 | D2 |
glass0 | 2.06 | 9 | 214 | 70 | 144 | D3 |
LanSat | 2.12 | 36 | 6 435 | 2 061 | 4 374 | D4 |
Abalone | 2.20 | 8 | 4 177 | 1 307 | 2 870 | D5 |
yeast1 | 2.46 | 8 | 1 484 | 429 | 1 055 | D6 |
glass-0-1-2-3_vs_4-5-6 | 3.20 | 9 | 214 | 51 | 163 | D7 |
ecoli1 | 3.36 | 7 | 336 | 77 | 259 | D8 |
new-thyroid2 | 5.14 | 5 | 215 | 35 | 180 | D9 |
ecoli2 | 5.46 | 7 | 336 | 52 | 284 | D10 |
sgment0 | 6.02 | 19 | 2 308 | 329 | 1 979 | D11 |
yeast3 | 8.10 | 8 | 1 484 | 163 | 1 321 | D12 |
ecoli3 | 8.60 | 7 | 336 | 35 | 301 | D13 |
yeast-2_vs_4 | 9.08 | 8 | 514 | 51 | 463 | D14 |
Pendigits | 9.42 | 16 | 10 992 | 1 055 | 9 937 | D15 |
vowel0 | 9.98 | 13 | 988 | 90 | 898 | D16 |
MNIST384 | 10.00 | 784 | 15 544 | 1 413 | 14 131 | D17 |
shuttle-c0-vs-c4 | 13.87 | 9 | 1 829 | 123 | 1 706 | D18 |
glass-0-1-6_vs_5 | 19.44 | 9 | 184 | 9 | 175 | D19 |
shuttle-c2-vs-c4 | 20.50 | 9 | 129 | 6 | 123 | D20 |
yeast5 | 32.73 | 8 | 1 484 | 44 | 1 440 | D21 |
ecoli-0-1-3-7_vs_2-6 | 39.14 | 7 | 281 | 7 | 274 | D22 |
实际情况 | 预测结果 | |
---|---|---|
预测正类 | 预测负类 | |
实际正类 | True Positive(TP) | False Negative(FN) |
实际负类 | False Positive(FP) | True Negative(TN) |
Tab.2 Confusion matrix
实际情况 | 预测结果 | |
---|---|---|
预测正类 | 预测负类 | |
实际正类 | True Positive(TP) | False Negative(FN) |
实际负类 | False Positive(FP) | True Negative(TN) |
数据集 | ROS | ADASYN | SMOTE | RBO-TL | RBO-IPF | RBU | RBO | 本文方法 |
---|---|---|---|---|---|---|---|---|
D1 | 94.12±2.30 | 94.69±1.26 | 91.76±2.53 | 93.61±2.23 | 93.83±1.81 | 92.95±3.59 | 94.27±2.69 | 96.20±2.35 |
D2 | 94.38±3.60 | 91.55±3.23 | 96.20±2.35 | 97.98±2.68 | 97.38±3.77 | 94.91±2.56 | 94.95±3.21 | 97.98±2.68 |
D3 | 66.55±11.55 | 71.44±9.78 | 68.56±7.37 | 69.60±8.50 | 67.91±4.29 | 67.70±7.35 | 70.21±4.12 | 70.77±4.94 |
D4 | 81.49±9.42 | 78.62±2.90 | 80.89±3.54 | 81.14±10.20 | 86.04±7.37 | 78.01±1.21 | 86.11±2.06 | 85.94±1.98 |
D5 | 43.28±13.67 | 50.92±8.61 | 52.12±5.85 | 47.36±6.04 | 51.15±14.11 | 41.36±8.33 | 44.29±9.56 | 52.24±31.61 |
D6 | 53.75±4.02 | 55.78±5.31 | 53.56±2.06 | 46.32±5.89 | 52.92±5.21 | 56.33±3.39 | 52.97±2.63 | 54.19±4.15 |
D7 | 83.84±6.28 | 83.05±5.54 | 85.14±8.46 | 84.72±4.75 | 87.47±3.74 | 83.73±8.82 | 86.30±1.56 | 90.08±8.65 |
D8 | 77.94±7.71 | 75.85±6.66 | 79.34±6.93 | 77.05±6.35 | 77.14±10.18 | 74.77±6.02 | 76.48±11.25 | 78.24±6.19 |
D9 | 87.60±11.41 | 91.56±7.36 | 86.95±5.52 | 88.24±13.63 | 88.44±13.70 | 77.89±12.48 | 88.24±13.63 | 91.37±8.60 |
D10 | 72.64±9.25 | 71.23±8.79 | 76.27±17.72 | 79.00±9.31 | 81.97±7.31 | 68.74±8.35 | 79.84±3.76 | 82.35±7.08 |
D11 | 98.17±1.04 | 97.91±0.72 | 97.92±1.18 | 98.02±1.40 | 97.58±1.30 | 95.24±2.76 | 97.87±1.47 | 98.20±1.38 |
D12 | 73.93±6.90 | 72.74±6.21 | 71.25±4.74 | 70.73±5.75 | 75.84±7.13 | 64.06±6.74 | 75.00±5.59 | 74.82±5.38 |
D13 | 52.72±13.27 | 59.27±7.58 | 62.13±9.21 | 57.54±13.16 | 59.52±17.94 | 52.66±7.46 | 49.48±8.29 | 61.88±2.42 |
D14 | 77.48±4.21 | 67.81±6.34 | 69.56±9.03 | 73.63±9.93 | 75.71±6.34 | 68.85±2.22 | 70.95±1.18 | 79.38±7.25 |
D15 | 80.28±10.22 | 81.40±10.69 | 79.42±11.09 | 84.45±9.34 | 85.88±8.25 | 81.40±10.69 | 84.59±9.17 | 87.72±9.29 |
D16 | 91.40±7.02 | 91.25±3.43 | 90.68±4.11 | 93.08±7.22 | 93.08±7.22 | 70.07±3.80 | 93.08±7.22 | 92.51±6.97 |
D17 | 87.89±5.69 | 79.18±7.86 | 91.43±7.00 | 89.40±6.88 | 91.81±7.91 | 87.78±9.22 | 87.89±12.48 | 92.73±4.53 |
D18 | 99.61±0.78 | 98.42±1.49 | 99.22±0.96 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 |
D19 | 62.00±34.87 | 68.86±18.57 | 63.33±22.11 | 66.00±37.74 | 62.00±34.87 | 59.69±33.86 | 66.00±37.74 | 88.00±9.80 |
D20 | 100.00±0.00 | 96.00±8.00 | 90.00±20.00 | 100.00±0.00 | 100.00±0.00 | 86.67±26.67 | 100.00±0.00 | 100.00±0.00 |
D21 | 70.01±8.38 | 64.89±3.56 | 68.03±10.55 | 72.64±8.86 | 75.91±5.61 | 55.31±8.34 | 81.49±9.42 | 80.77±7.44 |
D22 | 48.10±24.85 | 32.22±27.31 | 41.33±22.37 | 31.43±25.79 | 48.33±32.32 | 9.94±6.61 | 33.33±27.89 | 50.00±31.62 |
Tab.3 Comparison between proposed method and other seven methods on F1 value with Adaboost classifier
数据集 | ROS | ADASYN | SMOTE | RBO-TL | RBO-IPF | RBU | RBO | 本文方法 |
---|---|---|---|---|---|---|---|---|
D1 | 94.12±2.30 | 94.69±1.26 | 91.76±2.53 | 93.61±2.23 | 93.83±1.81 | 92.95±3.59 | 94.27±2.69 | 96.20±2.35 |
D2 | 94.38±3.60 | 91.55±3.23 | 96.20±2.35 | 97.98±2.68 | 97.38±3.77 | 94.91±2.56 | 94.95±3.21 | 97.98±2.68 |
D3 | 66.55±11.55 | 71.44±9.78 | 68.56±7.37 | 69.60±8.50 | 67.91±4.29 | 67.70±7.35 | 70.21±4.12 | 70.77±4.94 |
D4 | 81.49±9.42 | 78.62±2.90 | 80.89±3.54 | 81.14±10.20 | 86.04±7.37 | 78.01±1.21 | 86.11±2.06 | 85.94±1.98 |
D5 | 43.28±13.67 | 50.92±8.61 | 52.12±5.85 | 47.36±6.04 | 51.15±14.11 | 41.36±8.33 | 44.29±9.56 | 52.24±31.61 |
D6 | 53.75±4.02 | 55.78±5.31 | 53.56±2.06 | 46.32±5.89 | 52.92±5.21 | 56.33±3.39 | 52.97±2.63 | 54.19±4.15 |
D7 | 83.84±6.28 | 83.05±5.54 | 85.14±8.46 | 84.72±4.75 | 87.47±3.74 | 83.73±8.82 | 86.30±1.56 | 90.08±8.65 |
D8 | 77.94±7.71 | 75.85±6.66 | 79.34±6.93 | 77.05±6.35 | 77.14±10.18 | 74.77±6.02 | 76.48±11.25 | 78.24±6.19 |
D9 | 87.60±11.41 | 91.56±7.36 | 86.95±5.52 | 88.24±13.63 | 88.44±13.70 | 77.89±12.48 | 88.24±13.63 | 91.37±8.60 |
D10 | 72.64±9.25 | 71.23±8.79 | 76.27±17.72 | 79.00±9.31 | 81.97±7.31 | 68.74±8.35 | 79.84±3.76 | 82.35±7.08 |
D11 | 98.17±1.04 | 97.91±0.72 | 97.92±1.18 | 98.02±1.40 | 97.58±1.30 | 95.24±2.76 | 97.87±1.47 | 98.20±1.38 |
D12 | 73.93±6.90 | 72.74±6.21 | 71.25±4.74 | 70.73±5.75 | 75.84±7.13 | 64.06±6.74 | 75.00±5.59 | 74.82±5.38 |
D13 | 52.72±13.27 | 59.27±7.58 | 62.13±9.21 | 57.54±13.16 | 59.52±17.94 | 52.66±7.46 | 49.48±8.29 | 61.88±2.42 |
D14 | 77.48±4.21 | 67.81±6.34 | 69.56±9.03 | 73.63±9.93 | 75.71±6.34 | 68.85±2.22 | 70.95±1.18 | 79.38±7.25 |
D15 | 80.28±10.22 | 81.40±10.69 | 79.42±11.09 | 84.45±9.34 | 85.88±8.25 | 81.40±10.69 | 84.59±9.17 | 87.72±9.29 |
D16 | 91.40±7.02 | 91.25±3.43 | 90.68±4.11 | 93.08±7.22 | 93.08±7.22 | 70.07±3.80 | 93.08±7.22 | 92.51±6.97 |
D17 | 87.89±5.69 | 79.18±7.86 | 91.43±7.00 | 89.40±6.88 | 91.81±7.91 | 87.78±9.22 | 87.89±12.48 | 92.73±4.53 |
D18 | 99.61±0.78 | 98.42±1.49 | 99.22±0.96 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 |
D19 | 62.00±34.87 | 68.86±18.57 | 63.33±22.11 | 66.00±37.74 | 62.00±34.87 | 59.69±33.86 | 66.00±37.74 | 88.00±9.80 |
D20 | 100.00±0.00 | 96.00±8.00 | 90.00±20.00 | 100.00±0.00 | 100.00±0.00 | 86.67±26.67 | 100.00±0.00 | 100.00±0.00 |
D21 | 70.01±8.38 | 64.89±3.56 | 68.03±10.55 | 72.64±8.86 | 75.91±5.61 | 55.31±8.34 | 81.49±9.42 | 80.77±7.44 |
D22 | 48.10±24.85 | 32.22±27.31 | 41.33±22.37 | 31.43±25.79 | 48.33±32.32 | 9.94±6.61 | 33.33±27.89 | 50.00±31.62 |
数据集 | ROS | ADASYN | SMOTE | RBO-TL | RBO-IPF | RBU | RBO | 本文方法 |
---|---|---|---|---|---|---|---|---|
D1 | 96.43±1.66 | 97.01±0.98 | 95.92±1.52 | 94.84±2.01 | 95.56±1.48 | 95.94±1.69 | 96.18±2.19 | 97.22±1.74 |
D2 | 96.18±2.27 | 94.36±2.09 | 97.22±1.74 | 98.31±2.14 | 97.97±2.71 | 96.54±1.86 | 96.54±2.15 | 98.31±2.14 |
D3 | 74.25±9.36 | 79.10±8.16 | 76.45±6.11 | 77.56±7.60 | 76.31±3.52 | 75.70±6.26 | 77.45±3.21 | 78.65±4.48 |
D4 | 92.10±1.18 | 93.86±2.02 | 94.23±1.69 | 91.83±6.69 | 92.25±6.19 | 92.68±1.41 | 95.02±0.85 | 96.86±1.08 |
D5 | 61.55±11.91 | 72.15±7.03 | 76.30±5.86 | 74.32±8.32 | 75.27±7.79 | 62.21±12.67 | 63.08±10.10 | 76.66±36.54 |
D6 | 65.81±3.36 | 68.41±4.41 | 66.05±1.78 | 58.86±5.02 | 64.82±4.02 | 68.59±2.88 | 65.35±1.97 | 65.69±3.75 |
D7 | 88.59±4.45 | 88.28±4.16 | 90.20±6.45 | 88.83±3.76 | 91.82±2.19 | 89.70±6.12 | 90.74±1.70 | 93.33±5.75 |
D8 | 85.68±5.10 | 86.11±3.21 | 87.78±5.28 | 84.41±3.60 | 84.37±7.95 | 84.82±4.35 | 84.99±9.13 | 89.03±4.10 |
D9 | 93.68±5.13 | 98.02±1.95 | 94.84±3.62 | 93.68±5.85 | 94.88±6.11 | 92.90±5.99 | 93.68±5.85 | 96.83±3.48 |
D10 | 83.87±6.22 | 85.20±7.06 | 88.48±13.40 | 87.73±7.90 | 89.36±5.75 | 84.51±5.96 | 87.92±4.06 | 91.74±5.39 |
D11 | 98.93±0.89 | 99.26±0.31 | 99.52±0.38 | 98.78±0.85 | 98.70±0.83 | 98.66±0.63 | 98.75±0.85 | 99.19±0.67 |
D12 | 86.55±5.80 | 87.80±3.26 | 86.77±1.96 | 82.38±2.85 | 86.24±5.37 | 82.52±6.42 | 84.21±3.80 | 88.19±2.29 |
D13 | 70.61±11.57 | 79.25±7.42 | 83.52±9.90 | 73.22±10.07 | 73.01±13.91 | 71.44±11.56 | 65.62±7.83 | 87.16±5.17 |
D14 | 88.23±4.13 | 85.79±4.10 | 85.80±7.38 | 87.42±6.26 | 86.79±6.45 | 87.67±4.23 | 84.55±4.53 | 88.22±6.10 |
D15 | 88.18±6.28 | 89.99±8.57 | 86.20±7.90 | 89.02±4.07 | 89.42±5.49 | 87.99±8.57 | 90.00±6.26 | 92.39±5.79 |
D16 | 95.43±5.92 | 98.05±1.51 | 97.47±2.36 | 96.62±6.06 | 96.62±6.06 | 95.12±0.70 | 96.62±6.06 | 96.06±5.87 |
D17 | 94.77±4.27 | 92.58±3.93 | 95.01±3.56 | 95.97±4.41 | 96.06±5.87 | 91.21±4.71 | 92.90±5.99 | 97.17±1.58 |
D18 | 99.97±0.06 | 99.88±0.11 | 99.94±0.07 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 |
D19 | 73.36±38.41 | 97.38±1.71 | 91.78±12.14 | 73.65±38.60 | 73.36±38.41 | 88.07±13.69 | 73.65±38.60 | 99.14±0.71 |
D20 | 100.00±0.00 | 99.58±0.84 | 98.26±3.49 | 100.00±0.00 | 100.00±0.00 | 98.33±3.34 | 100.00±0.00 | 100.00±0.00 |
D21 | 85.55±7.89 | 92.67±4.06 | 92.73±5.55 | 90.05±4.96 | 92.29±2.14 | 85.98±8.11 | 90.11±4.97 | 93.34±0.38 |
D22 | 73.09±38.17 | 52.90±44.33 | 72.59±37.94 | 53.09±44.51 | 71.28±38.30 | 60.05±30.87 | 53.28±44.70 | 72.90±38.06 |
Tab.4 Comparison between proposed method and other seven methods on G?mean with Adaboost classifier
数据集 | ROS | ADASYN | SMOTE | RBO-TL | RBO-IPF | RBU | RBO | 本文方法 |
---|---|---|---|---|---|---|---|---|
D1 | 96.43±1.66 | 97.01±0.98 | 95.92±1.52 | 94.84±2.01 | 95.56±1.48 | 95.94±1.69 | 96.18±2.19 | 97.22±1.74 |
D2 | 96.18±2.27 | 94.36±2.09 | 97.22±1.74 | 98.31±2.14 | 97.97±2.71 | 96.54±1.86 | 96.54±2.15 | 98.31±2.14 |
D3 | 74.25±9.36 | 79.10±8.16 | 76.45±6.11 | 77.56±7.60 | 76.31±3.52 | 75.70±6.26 | 77.45±3.21 | 78.65±4.48 |
D4 | 92.10±1.18 | 93.86±2.02 | 94.23±1.69 | 91.83±6.69 | 92.25±6.19 | 92.68±1.41 | 95.02±0.85 | 96.86±1.08 |
D5 | 61.55±11.91 | 72.15±7.03 | 76.30±5.86 | 74.32±8.32 | 75.27±7.79 | 62.21±12.67 | 63.08±10.10 | 76.66±36.54 |
D6 | 65.81±3.36 | 68.41±4.41 | 66.05±1.78 | 58.86±5.02 | 64.82±4.02 | 68.59±2.88 | 65.35±1.97 | 65.69±3.75 |
D7 | 88.59±4.45 | 88.28±4.16 | 90.20±6.45 | 88.83±3.76 | 91.82±2.19 | 89.70±6.12 | 90.74±1.70 | 93.33±5.75 |
D8 | 85.68±5.10 | 86.11±3.21 | 87.78±5.28 | 84.41±3.60 | 84.37±7.95 | 84.82±4.35 | 84.99±9.13 | 89.03±4.10 |
D9 | 93.68±5.13 | 98.02±1.95 | 94.84±3.62 | 93.68±5.85 | 94.88±6.11 | 92.90±5.99 | 93.68±5.85 | 96.83±3.48 |
D10 | 83.87±6.22 | 85.20±7.06 | 88.48±13.40 | 87.73±7.90 | 89.36±5.75 | 84.51±5.96 | 87.92±4.06 | 91.74±5.39 |
D11 | 98.93±0.89 | 99.26±0.31 | 99.52±0.38 | 98.78±0.85 | 98.70±0.83 | 98.66±0.63 | 98.75±0.85 | 99.19±0.67 |
D12 | 86.55±5.80 | 87.80±3.26 | 86.77±1.96 | 82.38±2.85 | 86.24±5.37 | 82.52±6.42 | 84.21±3.80 | 88.19±2.29 |
D13 | 70.61±11.57 | 79.25±7.42 | 83.52±9.90 | 73.22±10.07 | 73.01±13.91 | 71.44±11.56 | 65.62±7.83 | 87.16±5.17 |
D14 | 88.23±4.13 | 85.79±4.10 | 85.80±7.38 | 87.42±6.26 | 86.79±6.45 | 87.67±4.23 | 84.55±4.53 | 88.22±6.10 |
D15 | 88.18±6.28 | 89.99±8.57 | 86.20±7.90 | 89.02±4.07 | 89.42±5.49 | 87.99±8.57 | 90.00±6.26 | 92.39±5.79 |
D16 | 95.43±5.92 | 98.05±1.51 | 97.47±2.36 | 96.62±6.06 | 96.62±6.06 | 95.12±0.70 | 96.62±6.06 | 96.06±5.87 |
D17 | 94.77±4.27 | 92.58±3.93 | 95.01±3.56 | 95.97±4.41 | 96.06±5.87 | 91.21±4.71 | 92.90±5.99 | 97.17±1.58 |
D18 | 99.97±0.06 | 99.88±0.11 | 99.94±0.07 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 |
D19 | 73.36±38.41 | 97.38±1.71 | 91.78±12.14 | 73.65±38.60 | 73.36±38.41 | 88.07±13.69 | 73.65±38.60 | 99.14±0.71 |
D20 | 100.00±0.00 | 99.58±0.84 | 98.26±3.49 | 100.00±0.00 | 100.00±0.00 | 98.33±3.34 | 100.00±0.00 | 100.00±0.00 |
D21 | 85.55±7.89 | 92.67±4.06 | 92.73±5.55 | 90.05±4.96 | 92.29±2.14 | 85.98±8.11 | 90.11±4.97 | 93.34±0.38 |
D22 | 73.09±38.17 | 52.90±44.33 | 72.59±37.94 | 53.09±44.51 | 71.28±38.30 | 60.05±30.87 | 53.28±44.70 | 72.90±38.06 |
数据集 | ROS | ADASYN | SMOTE | RBO-TL | RBO-IPF | RBU | RBO | 本文方法 |
---|---|---|---|---|---|---|---|---|
D1 | 92.62±1.17 | 94.60±1.01 | 94.11±2.04 | 95.31±2.14 | 95.90±1.80 | 93.60±1.24 | 92.78±2.08 | 94.83±2.97 |
D2 | 95.55±1.52 | 91.07±6.51 | 93.76±1.80 | 97.98±2.68 | 97.37±2.49 | 96.73±2.11 | 97.98±2.68 | 96.28±5.87 |
D3 | 70.11±8.29 | 58.52±6.89 | 71.00±3.59 | 64.89±15.42 | 64.38±14.11 | 70.07±6.23 | 65.27±6.62 | 74.57±8.08 |
D4 | 76.67±8.74 | 75.20±5.56 | 78.90±8.52 | 79.18±7.86 | 77.78±9.22 | 74.57±5.97 | 75.30±9.65 | 82.70±4.78 |
D5 | 62.68±11.55 | 71.41±12.40 | 72.91±10.06 | 73.96±11.02 | 74.46±8.22 | 67.48±12.82 | 51.89±8.46 | 74.76±11.32 |
D6 | 51.46±3.57 | 42.82±6.72 | 49.92±5.27 | 52.96±3.63 | 54.36±4.32 | 43.64±4.36 | 49.74±2.16 | 54.58±3.38 |
D7 | 87.92±6.38 | 84.60±6.34 | 86.25±7.27 | 87.52±6.74 | 87.09±8.75 | 79.44±6.91 | 88.80±5.82 | 91.23±8.16 |
D8 | 81.20±6.67 | 74.03±8.66 | 81.68±7.56 | 79.25±6.30 | 74.97±6.61 | 72.01±8.42 | 79.80±11.30 | 79.34±4.34 |
D9 | 90.07±6.77 | 90.27±6.91 | 91.98±5.05 | 90.07±6.67 | 91.81±7.91 | 81.23±13.24 | 88.24±13.63 | 94.27±5.36 |
D10 | 78.86±9.81 | 79.82±8.01 | 80.38±1.34 | 76.16±10.38 | 80.51±5.18 | 67.70±7.74 | 78.95±8.17 | 82.08±4.57 |
D11 | 97.89±1.11 | 97.02±1.64 | 98.80±0.37 | 97.61±1.09 | 98.18±0.61 | 96.31±1.02 | 98.32±0.90 | 98.33±0.57 |
D12 | 72.27±5.75 | 73.00±6.16 | 71.80±6.42 | 72.34±5.97 | 73.38±6.82 | 68.36±6.68 | 69.28±4.45 | 74.05±5.58 |
D13 | 62.56±13.27 | 53.96±8.59 | 63.14±4.96 | 53.29±9.61 | 61.09±18.26 | 53.28±4.03 | 60.34±12.85 | 67.24±3.61 |
D14 | 74.33±10.24 | 71.64±12.81 | 63.91±6.77 | 71.20±6.08 | 74.85±5.23 | 74.28±4.17 | 72.08±3.81 | 73.23±10.23 |
D15 | 80.28±10.22 | 81.40±10.96 | 79.42±11.09 | 85.33±12.93 | 84.63±9.77 | 81.40±10.96 | 84.59±9.17 | 87.72±9.29 |
D16 | 91.37±7.21 | 91.50±5.11 | 89.92±5.63 | 90.25±7.61 | 87.93±8.00 | 75.95±7.51 | 90.25±7.61 | 93.06±6.02 |
D17 | 87.87±7.92 | 87.07±10.68 | 92.46±5.32 | 97.88±1.39 | 97.25±1.73 | 88.60±8.83 | 93.33±7.30 | 94.00±6.10 |
D18 | 99.61±0.78 | 98.42±1.49 | 99.22±0.96 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 |
D19 | 62.00±34.87 | 61.43±16.11 | 47.33±27.36 | 64.00±38.78 | 69.33±36.90 | 45.10±29.35 | 66.00±37.74 | 72.00±37.09 |
D20 | 100.00±0.00 | 100.00±0.00 | 96.00±8.00 | 100.00±0.00 | 100.00±0.00 | 80.00±40.00 | 100.00±0.00 | 100.00±0.00 |
D21 | 70.95±6.14 | 64.95±6.33 | 66.60±4.37 | 71.25±6.69 | 70.22±4.06 | 48.22±6.34 | 69.30±9.37 | 71.84±7.01 |
D22 | 48.10±24.85 | 35.56±30.14 | 24.89±20.39 | 43.33±22.61 | 46.00±40.79 | 28.41±36.38 | 21.43±26.34 | 59.33±33.89 |
Tab.5 Comparison between proposed method and other seven methods on F1 value with BalanceCascade classifier
数据集 | ROS | ADASYN | SMOTE | RBO-TL | RBO-IPF | RBU | RBO | 本文方法 |
---|---|---|---|---|---|---|---|---|
D1 | 92.62±1.17 | 94.60±1.01 | 94.11±2.04 | 95.31±2.14 | 95.90±1.80 | 93.60±1.24 | 92.78±2.08 | 94.83±2.97 |
D2 | 95.55±1.52 | 91.07±6.51 | 93.76±1.80 | 97.98±2.68 | 97.37±2.49 | 96.73±2.11 | 97.98±2.68 | 96.28±5.87 |
D3 | 70.11±8.29 | 58.52±6.89 | 71.00±3.59 | 64.89±15.42 | 64.38±14.11 | 70.07±6.23 | 65.27±6.62 | 74.57±8.08 |
D4 | 76.67±8.74 | 75.20±5.56 | 78.90±8.52 | 79.18±7.86 | 77.78±9.22 | 74.57±5.97 | 75.30±9.65 | 82.70±4.78 |
D5 | 62.68±11.55 | 71.41±12.40 | 72.91±10.06 | 73.96±11.02 | 74.46±8.22 | 67.48±12.82 | 51.89±8.46 | 74.76±11.32 |
D6 | 51.46±3.57 | 42.82±6.72 | 49.92±5.27 | 52.96±3.63 | 54.36±4.32 | 43.64±4.36 | 49.74±2.16 | 54.58±3.38 |
D7 | 87.92±6.38 | 84.60±6.34 | 86.25±7.27 | 87.52±6.74 | 87.09±8.75 | 79.44±6.91 | 88.80±5.82 | 91.23±8.16 |
D8 | 81.20±6.67 | 74.03±8.66 | 81.68±7.56 | 79.25±6.30 | 74.97±6.61 | 72.01±8.42 | 79.80±11.30 | 79.34±4.34 |
D9 | 90.07±6.77 | 90.27±6.91 | 91.98±5.05 | 90.07±6.67 | 91.81±7.91 | 81.23±13.24 | 88.24±13.63 | 94.27±5.36 |
D10 | 78.86±9.81 | 79.82±8.01 | 80.38±1.34 | 76.16±10.38 | 80.51±5.18 | 67.70±7.74 | 78.95±8.17 | 82.08±4.57 |
D11 | 97.89±1.11 | 97.02±1.64 | 98.80±0.37 | 97.61±1.09 | 98.18±0.61 | 96.31±1.02 | 98.32±0.90 | 98.33±0.57 |
D12 | 72.27±5.75 | 73.00±6.16 | 71.80±6.42 | 72.34±5.97 | 73.38±6.82 | 68.36±6.68 | 69.28±4.45 | 74.05±5.58 |
D13 | 62.56±13.27 | 53.96±8.59 | 63.14±4.96 | 53.29±9.61 | 61.09±18.26 | 53.28±4.03 | 60.34±12.85 | 67.24±3.61 |
D14 | 74.33±10.24 | 71.64±12.81 | 63.91±6.77 | 71.20±6.08 | 74.85±5.23 | 74.28±4.17 | 72.08±3.81 | 73.23±10.23 |
D15 | 80.28±10.22 | 81.40±10.96 | 79.42±11.09 | 85.33±12.93 | 84.63±9.77 | 81.40±10.96 | 84.59±9.17 | 87.72±9.29 |
D16 | 91.37±7.21 | 91.50±5.11 | 89.92±5.63 | 90.25±7.61 | 87.93±8.00 | 75.95±7.51 | 90.25±7.61 | 93.06±6.02 |
D17 | 87.87±7.92 | 87.07±10.68 | 92.46±5.32 | 97.88±1.39 | 97.25±1.73 | 88.60±8.83 | 93.33±7.30 | 94.00±6.10 |
D18 | 99.61±0.78 | 98.42±1.49 | 99.22±0.96 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 |
D19 | 62.00±34.87 | 61.43±16.11 | 47.33±27.36 | 64.00±38.78 | 69.33±36.90 | 45.10±29.35 | 66.00±37.74 | 72.00±37.09 |
D20 | 100.00±0.00 | 100.00±0.00 | 96.00±8.00 | 100.00±0.00 | 100.00±0.00 | 80.00±40.00 | 100.00±0.00 | 100.00±0.00 |
D21 | 70.95±6.14 | 64.95±6.33 | 66.60±4.37 | 71.25±6.69 | 70.22±4.06 | 48.22±6.34 | 69.30±9.37 | 71.84±7.01 |
D22 | 48.10±24.85 | 35.56±30.14 | 24.89±20.39 | 43.33±22.61 | 46.00±40.79 | 28.41±36.38 | 21.43±26.34 | 59.33±33.89 |
数据集 | ROS | ADASYN | SMOTE | RBO-TL | RBO-IPF | RBU | RBO | 本文方法 |
---|---|---|---|---|---|---|---|---|
D1 | 95.12±0.79 | 96.00±0.90 | 95.96±1.34 | 96.31±2.40 | 95.78±1.80 | 95.35±1.16 | 94.79±1.90 | 96.68±2.44 |
D2 | 96.87±1.25 | 94.06±4.61 | 95.81±1.38 | 98.31±2.14 | 97.95±1.97 | 97.60±1.71 | 98.31±2.14 | 97.26±4.00 |
D3 | 77.05±6.40 | 67.48±6.36 | 78.45±3.31 | 73.69±12.81 | 72.23±10.70 | 78.31±5.24 | 74.07±5.39 | 81.91±7.18 |
D4 | 90.16±5.03 | 89.20±4.37 | 88.66±6.90 | 90.34±6.65 | 91.78±4.60 | 89.05±4.60 | 84.45±9.43 | 92.59±3.86 |
D5 | 86.62±10.40 | 85.02±11.74 | 82.36±10.70 | 81.34±11.64 | 83.43±12.86 | 85.25±9.78 | 82.18±6.49 | 86.63±6.93 |
D6 | 63.83±2.77 | 55.61±6.71 | 61.85±5.29 | 65.46±3.13 | 64.11±3.62 | 56.92±3.69 | 61.50±1.33 | 65.83±3.03 |
D7 | 91.20±4.44 | 90.62±5.56 | 91.23±5.91 | 91.00±6.94 | 90.97±6.11 | 86.09±5.14 | 92.09±5.56 | 94.40±5.14 |
D8 | 87.65±5.75 | 82.93±7.16 | 87.65±4.76 | 86.88±5.56 | 83.14±5.52 | 80.77±6.27 | 86.21±8.29 | 89.57±2.92 |
D9 | 93.29±3.49 | 97.74±1.71 | 97.13±3.06 | 93.29±3.49 | 95.97±4.41 | 92.91±6.45 | 93.68±5.85 | 96.49±3.78 |
D10 | 87.80±7.21 | 89.55±5.19 | 88.98±1.72 | 86.40±6.74 | 88.23±6.43 | 81.01±8.26 | 87.03±4.49 | 90.96±2.74 |
D11 | 99.01±0.70 | 98.73±0.78 | 99.67±0.28 | 99.09±0.37 | 98.93±0.54 | 98.86±0.54 | 98.83±0.63 | 99.09±0.45 |
D12 | 85.82±3.70 | 86.78±3.48 | 85.69±3.65 | 85.75±4.57 | 87.18±6.09 | 83.14±4.57 | 80.52±5.19 | 86.67±3.25 |
D13 | 78.33±10.42 | 76.68±5.69 | 82.88±5.76 | 74.15±7.16 | 78.75±15.73 | 72.73±5.03 | 73.17±12.27 | 89.57±6.21 |
D14 | 86.49±8.52 | 86.96±7.60 | 79.51±7.18 | 84.48±6.93 | 84.06±4.89 | 85.90±5.67 | 84.62±5.39 | 85.45±8.12 |
D15 | 88.18±6.28 | 87.99±8.57 | 86.20±7.90 | 90.97±7.71 | 91.63±4.23 | 87.99±8.57 | 90.00±6.26 | 92.39±5.79 |
D16 | 94.92±5.66 | 98.55±1.45 | 97.38±1.74 | 94.19±6.72 | 94.43±6.95 | 95.15±2.78 | 94.19±6.72 | 96.62±5.94 |
D17 | 95.98±3.10 | 95.73±4.33 | 94.86±5.77 | 98.87±1.39 | 97.66±0.63 | 95.11±4.12 | 95.37±1.85 | 96.34±5.95 |
D18 | 99.97±0.06 | 99.88±0.11 | 99.94±0.7 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 |
D19 | 73.36±38.41 | 91.70±11.57 | 72.29±37.84 | 73.44±38.62 | 73.85±38.58 | 86.85±10.73 | 73.65±38.60 | 79.42±39.72 |
D20 | 100.00±0.00 | 100.00±0.00 | 96.00±8.00 | 100.00±0.00 | 100.00±0.00 | 80.00±40.00 | 100.00±0.00 | 100.00±0.00 |
D21 | 84.63±6.50 | 91.36±7.07 | 94.04±2.04 | 89.27±5.50 | 89.75±2.73 | 76.89±6.96 | 85.71±5.41 | 92.07±4.46 |
D22 | 73.09±38.17 | 53.03±44.38 | 52.39±43.90 | 72.91±38.05 | 53.83±45.25 | 65.04±34.76 | 33.46±41.87 | 73.65±38.54 |
Tab.6 Comparison between proposed method and other seven methods on G?mean with BalanceCascade classifier
数据集 | ROS | ADASYN | SMOTE | RBO-TL | RBO-IPF | RBU | RBO | 本文方法 |
---|---|---|---|---|---|---|---|---|
D1 | 95.12±0.79 | 96.00±0.90 | 95.96±1.34 | 96.31±2.40 | 95.78±1.80 | 95.35±1.16 | 94.79±1.90 | 96.68±2.44 |
D2 | 96.87±1.25 | 94.06±4.61 | 95.81±1.38 | 98.31±2.14 | 97.95±1.97 | 97.60±1.71 | 98.31±2.14 | 97.26±4.00 |
D3 | 77.05±6.40 | 67.48±6.36 | 78.45±3.31 | 73.69±12.81 | 72.23±10.70 | 78.31±5.24 | 74.07±5.39 | 81.91±7.18 |
D4 | 90.16±5.03 | 89.20±4.37 | 88.66±6.90 | 90.34±6.65 | 91.78±4.60 | 89.05±4.60 | 84.45±9.43 | 92.59±3.86 |
D5 | 86.62±10.40 | 85.02±11.74 | 82.36±10.70 | 81.34±11.64 | 83.43±12.86 | 85.25±9.78 | 82.18±6.49 | 86.63±6.93 |
D6 | 63.83±2.77 | 55.61±6.71 | 61.85±5.29 | 65.46±3.13 | 64.11±3.62 | 56.92±3.69 | 61.50±1.33 | 65.83±3.03 |
D7 | 91.20±4.44 | 90.62±5.56 | 91.23±5.91 | 91.00±6.94 | 90.97±6.11 | 86.09±5.14 | 92.09±5.56 | 94.40±5.14 |
D8 | 87.65±5.75 | 82.93±7.16 | 87.65±4.76 | 86.88±5.56 | 83.14±5.52 | 80.77±6.27 | 86.21±8.29 | 89.57±2.92 |
D9 | 93.29±3.49 | 97.74±1.71 | 97.13±3.06 | 93.29±3.49 | 95.97±4.41 | 92.91±6.45 | 93.68±5.85 | 96.49±3.78 |
D10 | 87.80±7.21 | 89.55±5.19 | 88.98±1.72 | 86.40±6.74 | 88.23±6.43 | 81.01±8.26 | 87.03±4.49 | 90.96±2.74 |
D11 | 99.01±0.70 | 98.73±0.78 | 99.67±0.28 | 99.09±0.37 | 98.93±0.54 | 98.86±0.54 | 98.83±0.63 | 99.09±0.45 |
D12 | 85.82±3.70 | 86.78±3.48 | 85.69±3.65 | 85.75±4.57 | 87.18±6.09 | 83.14±4.57 | 80.52±5.19 | 86.67±3.25 |
D13 | 78.33±10.42 | 76.68±5.69 | 82.88±5.76 | 74.15±7.16 | 78.75±15.73 | 72.73±5.03 | 73.17±12.27 | 89.57±6.21 |
D14 | 86.49±8.52 | 86.96±7.60 | 79.51±7.18 | 84.48±6.93 | 84.06±4.89 | 85.90±5.67 | 84.62±5.39 | 85.45±8.12 |
D15 | 88.18±6.28 | 87.99±8.57 | 86.20±7.90 | 90.97±7.71 | 91.63±4.23 | 87.99±8.57 | 90.00±6.26 | 92.39±5.79 |
D16 | 94.92±5.66 | 98.55±1.45 | 97.38±1.74 | 94.19±6.72 | 94.43±6.95 | 95.15±2.78 | 94.19±6.72 | 96.62±5.94 |
D17 | 95.98±3.10 | 95.73±4.33 | 94.86±5.77 | 98.87±1.39 | 97.66±0.63 | 95.11±4.12 | 95.37±1.85 | 96.34±5.95 |
D18 | 99.97±0.06 | 99.88±0.11 | 99.94±0.7 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 |
D19 | 73.36±38.41 | 91.70±11.57 | 72.29±37.84 | 73.44±38.62 | 73.85±38.58 | 86.85±10.73 | 73.65±38.60 | 79.42±39.72 |
D20 | 100.00±0.00 | 100.00±0.00 | 96.00±8.00 | 100.00±0.00 | 100.00±0.00 | 80.00±40.00 | 100.00±0.00 | 100.00±0.00 |
D21 | 84.63±6.50 | 91.36±7.07 | 94.04±2.04 | 89.27±5.50 | 89.75±2.73 | 76.89±6.96 | 85.71±5.41 | 92.07±4.46 |
D22 | 73.09±38.17 | 53.03±44.38 | 52.39±43.90 | 72.91±38.05 | 53.83±45.25 | 65.04±34.76 | 33.46±41.87 | 73.65±38.54 |
数据集 | ROS | ADASYN | SMOTE | RBO-TL | RBO-IPF | RBU | RBO | 本文方法 |
---|---|---|---|---|---|---|---|---|
D1 | 93.84±3.13 | 97.98±2.68 | 96.73±2.11 | 95.11±2.04 | 95.75±2.11 | 97.98±2.68 | 97.37±2.49 | 97.98±2.68 |
D2 | 95.55±1.52 | 92.04±4.97 | 93.76±1.80 | 97.98±2.68 | 97.37±2.49 | 96.73±2.11 | 97.98±2.68 | 96.28±5.87 |
D3 | 70.11±8.29 | 60.43±4.53 | 71.00±3.59 | 64.38±15.09 | 64.38±14.11 | 70.07±6.23 | 65.27±6.62 | 74.57±8.08 |
D4 | 62.48±11.66 | 51.81±29.71 | 51.04±29.75 | 61.55±10.06 | 62.32±14.59 | 41.21±15.00 | 63.33±0.67 | 65.95±18.66 |
D5 | 79.82±8.01 | 80.38±1.34 | 80.32±11.32 | 80.20±5.95 | 79.66±2.61 | 81.23±13.24 | 67.70±7.74 | 82.08±4.57 |
D6 | 51.46±3.57 | 48.97±4.04 | 54.01±3.29 | 52.96±3.63 | 54.36±4.32 | 53.36±4.04 | 49.74±2.16 | 54.58±3.38 |
D7 | 87.92±6.38 | 87.01±3.28 | 86.25±7.27 | 87.52±6.74 | 87.09±8.75 | 79.44±6.91 | 88.80±5.82 | 91.23±8.16 |
D8 | 81.20±6.67 | 75.42±7.41 | 80.52±6.26 | 79.25±6.30 | 74.97±6.61 | 72.01±8.42 | 79.80±11.30 | 80.34±4.34 |
D9 | 90.07±6.77 | 90.27±6.91 | 92.33±4.64 | 90.07±6.77 | 91.81±7.91 | 81.23±13.24 | 88.24±13.63 | 94.27±5.36 |
D10 | 78.86±9.81 | 76.89±8.53 | 81.44±5.09 | 76.16±10.38 | 80.51±5.18 | 67.70±7.74 | 78.95±8.17 | 79.34±5.57 |
D11 | 98.04±1.31 | 97.02±1.64 | 98.80±0.37 | 97.61±1.09 | 98.18±0.61 | 96.75±0.96 | 98.47±1.09 | 98.33±0.57 |
D12 | 72.27±5.75 | 71.03±2.14 | 71.40±6.60 | 72.34±5.97 | 74.38±6.82 | 68.36±6.68 | 69.28±4.45 | 74.55±5.82 |
D13 | 62.56±13.27 | 53.96±8.59 | 59.81±6.75 | 53.29±9.61 | 61.09±18.26 | 53.28±4.03 | 60.34±12.85 | 67.90±3.74 |
D14 | 74.33±10.24 | 69.72±11.36 | 68.48±11.57 | 71.20±6.08 | 74.85±5.23 | 74.28±4.17 | 70.28±3.81 | 76.24±5.88 |
D15 | 71.64±12.81 | 63.91±6.77 | 73.16±4.23 | 71.80±6.42 | 74.28±4.17 | 63.03±11.62 | 73.20±5.09 | 73.23±10.23 |
D16 | 91.37±7.21 | 92.04±5.79 | 89.92±5.63 | 90.00±7.95 | 91.40±7.02 | 75.95±7.51 | 90.00±7.95 | 93.06±6.02 |
D17 | 94.56±0.78 | 94.95±1.79 | 93.81±3.24 | 93.95±2.11 | 92.88±2.58 | 95.86±1.71 | 92.69±1.89 | 95.86±1.71 |
D18 | 99.61±0.78 | 98.42±1.49 | 99.22±0.96 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 |
D19 | 62.00±34.87 | 63.43±13.78 | 62.76±20.56 | 64.00±38.78 | 69.33±36.90 | 45.10±29.35 | 66.00±37.74 | 88.00±9.80 |
D20 | 100.00±0.00 | 100.00±0.00 | 93.33±13.33 | 100.00±0.00 | 100.00±0.00 | 80.00±40.00 | 100.00±0.00 | 100.00±0.00 |
D21 | 70.95±6.14 | 63.47±6.61 | 65.57±4.81 | 71.25±6.69 | 70.22±4.06 | 48.22±6.34 | 69.30±9.37 | 71.84±7.01 |
D22 | 48.10±24.85 | 35.56±30.14 | 24.89±20.39 | 43.33±22.61 | 46.00±40.79 | 28.41±36.38 | 21.43±26.34 | 59.33±33.89 |
Tab.7 Comparison between proposed method and other seven methods on F1 value with Easyensemble classifier
数据集 | ROS | ADASYN | SMOTE | RBO-TL | RBO-IPF | RBU | RBO | 本文方法 |
---|---|---|---|---|---|---|---|---|
D1 | 93.84±3.13 | 97.98±2.68 | 96.73±2.11 | 95.11±2.04 | 95.75±2.11 | 97.98±2.68 | 97.37±2.49 | 97.98±2.68 |
D2 | 95.55±1.52 | 92.04±4.97 | 93.76±1.80 | 97.98±2.68 | 97.37±2.49 | 96.73±2.11 | 97.98±2.68 | 96.28±5.87 |
D3 | 70.11±8.29 | 60.43±4.53 | 71.00±3.59 | 64.38±15.09 | 64.38±14.11 | 70.07±6.23 | 65.27±6.62 | 74.57±8.08 |
D4 | 62.48±11.66 | 51.81±29.71 | 51.04±29.75 | 61.55±10.06 | 62.32±14.59 | 41.21±15.00 | 63.33±0.67 | 65.95±18.66 |
D5 | 79.82±8.01 | 80.38±1.34 | 80.32±11.32 | 80.20±5.95 | 79.66±2.61 | 81.23±13.24 | 67.70±7.74 | 82.08±4.57 |
D6 | 51.46±3.57 | 48.97±4.04 | 54.01±3.29 | 52.96±3.63 | 54.36±4.32 | 53.36±4.04 | 49.74±2.16 | 54.58±3.38 |
D7 | 87.92±6.38 | 87.01±3.28 | 86.25±7.27 | 87.52±6.74 | 87.09±8.75 | 79.44±6.91 | 88.80±5.82 | 91.23±8.16 |
D8 | 81.20±6.67 | 75.42±7.41 | 80.52±6.26 | 79.25±6.30 | 74.97±6.61 | 72.01±8.42 | 79.80±11.30 | 80.34±4.34 |
D9 | 90.07±6.77 | 90.27±6.91 | 92.33±4.64 | 90.07±6.77 | 91.81±7.91 | 81.23±13.24 | 88.24±13.63 | 94.27±5.36 |
D10 | 78.86±9.81 | 76.89±8.53 | 81.44±5.09 | 76.16±10.38 | 80.51±5.18 | 67.70±7.74 | 78.95±8.17 | 79.34±5.57 |
D11 | 98.04±1.31 | 97.02±1.64 | 98.80±0.37 | 97.61±1.09 | 98.18±0.61 | 96.75±0.96 | 98.47±1.09 | 98.33±0.57 |
D12 | 72.27±5.75 | 71.03±2.14 | 71.40±6.60 | 72.34±5.97 | 74.38±6.82 | 68.36±6.68 | 69.28±4.45 | 74.55±5.82 |
D13 | 62.56±13.27 | 53.96±8.59 | 59.81±6.75 | 53.29±9.61 | 61.09±18.26 | 53.28±4.03 | 60.34±12.85 | 67.90±3.74 |
D14 | 74.33±10.24 | 69.72±11.36 | 68.48±11.57 | 71.20±6.08 | 74.85±5.23 | 74.28±4.17 | 70.28±3.81 | 76.24±5.88 |
D15 | 71.64±12.81 | 63.91±6.77 | 73.16±4.23 | 71.80±6.42 | 74.28±4.17 | 63.03±11.62 | 73.20±5.09 | 73.23±10.23 |
D16 | 91.37±7.21 | 92.04±5.79 | 89.92±5.63 | 90.00±7.95 | 91.40±7.02 | 75.95±7.51 | 90.00±7.95 | 93.06±6.02 |
D17 | 94.56±0.78 | 94.95±1.79 | 93.81±3.24 | 93.95±2.11 | 92.88±2.58 | 95.86±1.71 | 92.69±1.89 | 95.86±1.71 |
D18 | 99.61±0.78 | 98.42±1.49 | 99.22±0.96 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 |
D19 | 62.00±34.87 | 63.43±13.78 | 62.76±20.56 | 64.00±38.78 | 69.33±36.90 | 45.10±29.35 | 66.00±37.74 | 88.00±9.80 |
D20 | 100.00±0.00 | 100.00±0.00 | 93.33±13.33 | 100.00±0.00 | 100.00±0.00 | 80.00±40.00 | 100.00±0.00 | 100.00±0.00 |
D21 | 70.95±6.14 | 63.47±6.61 | 65.57±4.81 | 71.25±6.69 | 70.22±4.06 | 48.22±6.34 | 69.30±9.37 | 71.84±7.01 |
D22 | 48.10±24.85 | 35.56±30.14 | 24.89±20.39 | 43.33±22.61 | 46.00±40.79 | 28.41±36.38 | 21.43±26.34 | 59.33±33.89 |
数据集 | ROS | ADASYN | SMOTE | RBO-TL | RBO-IPF | RBU | RBO | 本文方法 |
---|---|---|---|---|---|---|---|---|
D1 | 95.79±1.80 | 98.31±2.14 | 97.60±1.71 | 95.96±1.34 | 95.35±1.66 | 98.31±2.14 | 97.95±1.97 | 98.31±2.14 |
D2 | 96.87±1.25 | 94.79±3.41 | 95.81±1.38 | 98.31±2.14 | 97.95±1.97 | 97.60±1.71 | 98.31±2.14 | 97.26±4.00 |
D3 | 77.05±6.40 | 69.41±4.18 | 78.45±3.31 | 73.36±12.60 | 72.23±10.70 | 78.31±5.24 | 74.07±5.39 | 81.91±7.18 |
D4 | 79.25±11.59 | 73.35±37.29 | 73.66±37.53 | 79.86±16.74 | 78.18±17.66 | 80.83±7.56 | 80.54±15.15 | 80.36±16.11 |
D5 | 89.55±5.91 | 88.98±1.72 | 85.58±7.70 | 85.41±3.29 | 89.37±1.48 | 90.96±2.74 | 81.01±8.26 | 92.91±6.45 |
D6 | 63.83±2.77 | 61.69±3.11 | 65.63±2.51 | 65.46±3.13 | 64.11±3.62 | 65.24±3.39 | 61.50±1.33 | 65.83±3.03 |
D7 | 91.20±4.44 | 92.79±2.84 | 91.23±5.91 | 91.00±6.94 | 90.97±6.11 | 86.09±5.14 | 92.09±5.56 | 94.40±5.14 |
D8 | 87.65±5.75 | 84.89±4.73 | 88.67±5.03 | 86.88±5.56 | 83.14±5.52 | 80.77±6.27 | 86.21±8.29 | 89.57±2.92 |
D9 | 93.29±3.49 | 97.74±1.71 | 98.31±1.06 | 93.29±3.49 | 95.97±4.41 | 92.91±6.45 | 93.68±5.85 | 96.49±3.78 |
D10 | 87.80±7.21 | 88.90±5.46 | 90.04±4.91 | 86.40±6.74 | 88.23±6.43 | 81.01±8.26 | 87.03±4.49 | 90.27±2.52 |
D11 | 99.03±0.73 | 98.73±0.78 | 99.80±0.60 | 99.09±0.45 | 98.93±0.54 | 99.06±0.24 | 98.98±0.79 | 99.09±0.45 |
D12 | 85.82±3.70 | 87.30±1.59 | 85.33±3.97 | 85.75±4.57 | 87.18±6.09 | 83.14±4.57 | 80.52±5.19 | 87.31±3.53 |
D13 | 78.33±10.42 | 76.68±5.69 | 80.89±5.11 | 74.15±7.16 | 78.75±15.73 | 72.73±5.03 | 73.17±12.27 | 89.74±6.34 |
D14 | 86.49±8.52 | 85.09±5.58 | 84.62±8.92 | 84.48±6.93 | 84.06±4.89 | 85.90±5.67 | 84.62±5.39 | 86.95±5.23 |
D15 | 85.69±3.65 | 79.51±7.18 | 84.93±3.65 | 86.96±7.60 | 85.41±3.29 | 83.84±9.59 | 85.90±5.67 | 85.45±8.12 |
D16 | 94.92±5.66 | 98.60±1.50 | 97.38±1.74 | 94.76±5.67 | 95.43±5.92 | 95.15±2.78 | 94.76±5.67 | 96.62±5.94 |
D17 | 95.80±0.70 | 96.01±1.55 | 95.45±2.37 | 95.19±2.75 | 94.23±2.51 | 96.97±1.21 | 93.86±1.71 | 96.97±1.21 |
D18 | 99.97±0.06 | 99.88±0.11 | 99.94±0.07 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 |
D19 | 73.36±38.41 | 96.79±1.73 | 91.70±11.61 | 73.44±38.62 | 73.85±38.58 | 86.85±10.73 | 73.65±38.60 | 99.14±0.71 |
D20 | 100.00±0.00 | 100.00±0.00 | 99.15±1.70 | 100.00±0.00 | 100.00±0.00 | 80.00±40.00 | 100.00±0.00 | 100.00±0.00 |
D21 | 84.63±6.50 | 90.20±6.24 | 93.97±2.07 | 89.27±5.50 | 89.75±2.73 | 76.89±6.96 | 85.71±5.41 | 92.07±4.46 |
D22 | 73.09±38.17 | 53.03±44.38 | 52.39±43.90 | 72.91±38.05 | 53.83±45.25 | 65.04±34.67 | 33.46±41.87 | 73.65±38.54 |
Tab.8 Comparison between proposed method and other seven methods on G?mean with Easyensemble classifier
数据集 | ROS | ADASYN | SMOTE | RBO-TL | RBO-IPF | RBU | RBO | 本文方法 |
---|---|---|---|---|---|---|---|---|
D1 | 95.79±1.80 | 98.31±2.14 | 97.60±1.71 | 95.96±1.34 | 95.35±1.66 | 98.31±2.14 | 97.95±1.97 | 98.31±2.14 |
D2 | 96.87±1.25 | 94.79±3.41 | 95.81±1.38 | 98.31±2.14 | 97.95±1.97 | 97.60±1.71 | 98.31±2.14 | 97.26±4.00 |
D3 | 77.05±6.40 | 69.41±4.18 | 78.45±3.31 | 73.36±12.60 | 72.23±10.70 | 78.31±5.24 | 74.07±5.39 | 81.91±7.18 |
D4 | 79.25±11.59 | 73.35±37.29 | 73.66±37.53 | 79.86±16.74 | 78.18±17.66 | 80.83±7.56 | 80.54±15.15 | 80.36±16.11 |
D5 | 89.55±5.91 | 88.98±1.72 | 85.58±7.70 | 85.41±3.29 | 89.37±1.48 | 90.96±2.74 | 81.01±8.26 | 92.91±6.45 |
D6 | 63.83±2.77 | 61.69±3.11 | 65.63±2.51 | 65.46±3.13 | 64.11±3.62 | 65.24±3.39 | 61.50±1.33 | 65.83±3.03 |
D7 | 91.20±4.44 | 92.79±2.84 | 91.23±5.91 | 91.00±6.94 | 90.97±6.11 | 86.09±5.14 | 92.09±5.56 | 94.40±5.14 |
D8 | 87.65±5.75 | 84.89±4.73 | 88.67±5.03 | 86.88±5.56 | 83.14±5.52 | 80.77±6.27 | 86.21±8.29 | 89.57±2.92 |
D9 | 93.29±3.49 | 97.74±1.71 | 98.31±1.06 | 93.29±3.49 | 95.97±4.41 | 92.91±6.45 | 93.68±5.85 | 96.49±3.78 |
D10 | 87.80±7.21 | 88.90±5.46 | 90.04±4.91 | 86.40±6.74 | 88.23±6.43 | 81.01±8.26 | 87.03±4.49 | 90.27±2.52 |
D11 | 99.03±0.73 | 98.73±0.78 | 99.80±0.60 | 99.09±0.45 | 98.93±0.54 | 99.06±0.24 | 98.98±0.79 | 99.09±0.45 |
D12 | 85.82±3.70 | 87.30±1.59 | 85.33±3.97 | 85.75±4.57 | 87.18±6.09 | 83.14±4.57 | 80.52±5.19 | 87.31±3.53 |
D13 | 78.33±10.42 | 76.68±5.69 | 80.89±5.11 | 74.15±7.16 | 78.75±15.73 | 72.73±5.03 | 73.17±12.27 | 89.74±6.34 |
D14 | 86.49±8.52 | 85.09±5.58 | 84.62±8.92 | 84.48±6.93 | 84.06±4.89 | 85.90±5.67 | 84.62±5.39 | 86.95±5.23 |
D15 | 85.69±3.65 | 79.51±7.18 | 84.93±3.65 | 86.96±7.60 | 85.41±3.29 | 83.84±9.59 | 85.90±5.67 | 85.45±8.12 |
D16 | 94.92±5.66 | 98.60±1.50 | 97.38±1.74 | 94.76±5.67 | 95.43±5.92 | 95.15±2.78 | 94.76±5.67 | 96.62±5.94 |
D17 | 95.80±0.70 | 96.01±1.55 | 95.45±2.37 | 95.19±2.75 | 94.23±2.51 | 96.97±1.21 | 93.86±1.71 | 96.97±1.21 |
D18 | 99.97±0.06 | 99.88±0.11 | 99.94±0.07 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 | 100.00±0.00 |
D19 | 73.36±38.41 | 96.79±1.73 | 91.70±11.61 | 73.44±38.62 | 73.85±38.58 | 86.85±10.73 | 73.65±38.60 | 99.14±0.71 |
D20 | 100.00±0.00 | 100.00±0.00 | 99.15±1.70 | 100.00±0.00 | 100.00±0.00 | 80.00±40.00 | 100.00±0.00 | 100.00±0.00 |
D21 | 84.63±6.50 | 90.20±6.24 | 93.97±2.07 | 89.27±5.50 | 89.75±2.73 | 76.89±6.96 | 85.71±5.41 | 92.07±4.46 |
D22 | 73.09±38.17 | 53.03±44.38 | 52.39±43.90 | 72.91±38.05 | 53.83±45.25 | 65.04±34.67 | 33.46±41.87 | 73.65±38.54 |
1 | TARAWNEH A S, HASSANAT A B, ALTARAWNEH G A, et al. Stop oversampling for class imbalance learning: a review[J]. IEEE Access, 2022, 10: 47643-47660. |
2 | BRANCO P, TORGO L, RIBEIRO R P. Pre-processing approaches for imbalanced distributions in regression[J]. Neurocomputing, 2019, 343: 76-99. |
3 | WANG Z, WANG B, CHENG Y, et al. Cost-sensitive fuzzy multiple kernel learning for imbalanced problem[J]. Neurocomputing, 2019, 366: 178-193. |
4 | GAO X, REN B, ZHANG H, et al. An ensemble imbalanced classification method based on model dynamic selection driven by data partition hybrid sampling[J]. Expert Systems with Applications, 2020, 160: 113660. |
5 | SUN B, CHEN H, WANG J, et al. Evolutionary under-sampling based bagging ensemble method for imbalanced data classification[J]. Frontiers of Computer Science, 2018, 12: 331-350. |
6 | LI J, ZHU Q, WU Q, et al. A novel oversampling technique for class-imbalanced learning based on SMOTE and natural neighbors[J]. Information Sciences, 2021, 565: 438-455. |
7 | GARCÍA V, SÁNCHEZ J S, MARQUÉS A I, et al. Understanding the apparent superiority of over-sampling through an analysis of local information for class-imbalanced data[J]. Expert Systems with Applications, 2020, 158: 113026. |
8 | PAN T, ZHAO J, WU W, et al. Learning imbalanced datasets based on SMOTE and Gaussian distribution[J]. Information Sciences, 2020, 512: 1214-1233. |
9 | SHIN K, HAN J, KANG S. MI-MOTE: multiple imputation-based minority oversampling technique for imbalanced and incomplete data classification[J]. Information Sciences, 2021, 575: 80-89. |
10 | LIU C, JIN S, WANG D, et al. Constrained oversampling: an oversampling approach to reduce noise generation in imbalanced datasets with class overlapping[J]. IEEE Access, 2020, 10: 91452-91465. |
11 | ZHU T, LIN Y, LIU Y, et al. Minority oversampling for imbalanced ordinal regression[J]. Knowledge-Based Systems, 2019, 166: 140-155. |
12 | 雷明珠,王浩,贾蓉,等.基于特征间关系合成少数类样本的过采样算法[J].计算机应用, 2024, 44(5): 1428-1436. |
LEI M Z, WANG H, JIA R, et al. Oversampling algorithm based on synthesizing minority class samples using relationships between features[J/OL]. Journal of Computer Applications, 2024, 44(5): 1428-1436. | |
13 | EPENDI U, ROCHIM A F, WIBOWO A. A hybrid sampling approach for improving the classification of imbalanced data using ROS and NCL methods[J]. International Journal of Intelligent Engineering & Systems, 2023, 16(3): 345-361. |
14 | CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002, 16(1): 321-357. |
15 | HAN H, WANG W-Y, MAO B-H. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning[C]// Proceedings of the 2005 International Conference on Intelligent Computing. Berlin: Springer, 2005: 878-887. |
16 | HE H, BAI Y, GARCIA E A, et al. ADASYN: adaptive synthetic sampling approach for imbalanced learning[C]// Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). Piscataway: IEEE, 2008: 1322-1328. |
17 | LENG Q, GUO J, JIAO E, et al. NanBDOS: adaptive and parameter-free borderline oversampling via natural neighbor search for class-imbalance learning[J]. Knowledge-Based Systems, 2023, 274: 110665. |
18 | THEJAS G S, HARIPRASAD Y, IYENGAR S S, et al. An extension of synthetic minority oversampling technique based on Kalman filter for imbalanced datasets[J]. Machine Learning with Applications, 2022, 8: 100267. |
19 | KOZIARSKI M, KRAWCZYK B, WOŹNIAK M. Radial-based oversampling for noisy imbalanced data classification[J]. Neurocomputing, 2019, 343: 19-33. |
20 | 李蒙蒙,刘艺,李庚松,等.不平衡多分类算法综述[J].计算机应用,2022,42(11):3307-3321. |
LI M M, LIU Y, LI G S, et al. Survey on imbalanced multi-class classification algorithms [J]. Journal of Computer Applications, 2022, 42(11): 3307-3321. | |
21 | HE Z, TAO J, LENG Q, et al. HS-Gen: a hypersphere-constrained generation mechanism to improve synthetic minority oversampling for imbalanced classification[J]. Complex & Intelligent Systems, 2022, 9: 3971-3988. |
22 | 陶佳晴, 贺作伟, 冷强奎,等. 基于Tomek链的边界少数类样本合成过采样方法[J]. 计算机应用研究,2023, 40(2):463-469. |
TAO J Q, HE Z W, LENG Q K, et al. Synthetic oversampling method for boundary minority samples based on Tomek link[J]. Application Research of Computers, 2023, 40(2):463-469. | |
23 | 陆宇,赵凌云,白斌雯,等.基于改进的半监督聚类的不平衡分类算法[J].计算机应用,2022, 42(12): 3750-3755. |
LU Y, ZHAO L Y, BAI B W, et al. Imbalanced classification algorithm based on improved semi-supervised clustering [J]. Journal of Computer Applications, 2022,42(12): 3750-3755. | |
24 | MALDONADO S, VAIRETTI C, FERNANDEZ A, et al. FW-SMOTE: a feature-weighted oversampling approach for imbalanced classification[J]. Pattern Recognition, 2022, 124: 108511. |
25 | KOZIARSKI M. Radial-based undersampling for imbalanced data classification[J]. Pattern Recognition, 2020, 102: 107262. |
26 | TAO X, ZHENG Y, CHEN W, et al. SVDD-based weighted oversampling technique for imbalanced and overlapped dataset learning[J]. Information Sciences, 2022, 588: 13-51. |
27 | GNIP P, VOKOROKOS L, DROTÁR P. Selective oversampling approach for strongly imbalanced data[J]. PeerJ Computer Science, 2021, 7: e604. |
28 | WANG L, WU C. Dynamic imbalanced business credit evaluation based on Learn++ with sliding time window and weight sampling and FCM with multiple kernels[J]. Information Sciences, 2020, 520: 305-323. |
29 | ZHU Q, FENG J, HUANG J. Natural neighbor: a self-adaptive neighborhood method without parameter K [J]. Pattern Recognition Letters, 2016, 80: 30-36. |
30 | GUAN H, ZHANG Y, XIAN M, et al. SMOTE-WENN: solving class imbalance and small sample problems by oversampling and distance scaling[J]. Applied Intelligence, 2021, 51: 1394-1409. |
31 | SANTOS M S, ABREU P H, JAPKOWICZ N, et al. On the joint-effect of class imbalance and overlap: a critical review[J]. Artificial Intelligence Review, 2022, 55(8): 6207-6275. |
32 | KOZIARSKI M. RBO-IPF: addressing the noisy and borderline examples problem in imbalanced classification by Radial-based oversampling method with filtering[J]. Pattern Recognition, 2022, 120: 108114. |
33 | WANG L, XU S, WANG X, et al. Addressing class imbalance in federated learning[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(11): 10165-10173. |
34 | 孟东霞, 李玉鑑. 利用自然最近邻的不平衡数据过采样方法[J]. 计算机工程与应用, 2021, 57(2): 91-96. |
MENG D X, LI Y J. Oversampling method for unbalanced data using natural nearest neighbor[J]. Computer Engineering and Applications, 2021, 57(2): 91-96. | |
35 | YI X, XU Y, HU Q, et al. ASN-SMOTE: a synthetic minority oversampling method with adaptive qualified synthesizer selection[J]. Complex & Intelligent Systems, 2022, 8: 2247-2272. |
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