《计算机应用》唯一官方网站 ›› 2023, Vol. 43 ›› Issue (10): 3121-3128.DOI: 10.11772/j.issn.1001-9081.2022101543
所属专题: 数据科学与技术
收稿日期:
2022-10-17
修回日期:
2022-11-28
接受日期:
2022-11-30
发布日期:
2023-10-07
出版日期:
2023-10-10
通讯作者:
罗川
作者简介:
马磊(1997—),男(回族),四川成都人,硕士研究生,主要研究方向:特征选择基金资助:
Lei MA1, Chuan LUO1(), Tianrui LI2, Hongmei CHEN2
Received:
2022-10-17
Revised:
2022-11-28
Accepted:
2022-11-30
Online:
2023-10-07
Published:
2023-10-10
Contact:
Chuan LUO
About author:
MA Lei, born in 1997, M. S. candidate. His research interests include feature selection.Supported by:
摘要:
动态特征选择算法能够大幅提升处理动态数据的效率,然而目前基于模糊粗糙集的无监督的动态特征选择算法较少。针对上述问题,提出一种特征分批次到达情况下的基于模糊粗糙集的无监督动态特征选择(UDFRFS)算法。首先,通过定义伪三角范数和新的相似关系在已有数据的基础上进行模糊关系值的更新过程,从而减少不必要的运算过程;其次,通过利用已有的特征选择结果,在新的特征到达后,使用依赖度判断原始特征部分是否需要重新计算,以减少冗余的特征选择过程,从而进一步提高特征选择的速度。实验结果表明,UDFRFS相较于静态的基于依赖度的无监督模糊粗糙集特征选择算法,在时间效率方面能够提升90个百分点以上,同时保持较好的分类精度和聚类表现。
中图分类号:
马磊, 罗川, 李天瑞, 陈红梅. 基于模糊粗糙集的无监督动态特征选择算法[J]. 计算机应用, 2023, 43(10): 3121-3128.
Lei MA, Chuan LUO, Tianrui LI, Hongmei CHEN. Fuzzy-rough set based unsupervised dynamic feature selection algorithm[J]. Journal of Computer Applications, 2023, 43(10): 3121-3128.
数据集 | 样本数 | 特征数 | 聚类数 | 来源 |
---|---|---|---|---|
Olive Oil | 30 | 570 | 4 | TSC |
SCADI | 70 | 205 | 7 | UCI |
SAP | 131 | 21 | 3 | UCI |
Divorce | 170 | 54 | 2 | UCI |
Sonar | 208 | 60 | 2 | UCI |
Ionosphere | 351 | 33 | 2 | UCI |
Wdbc | 569 | 30 | 2 | UCI |
German | 1 000 | 20 | 2 | UCI |
Waveform | 5 000 | 21 | 3 | UCI |
表1 数据集详情
Tab. 1 Dataset details
数据集 | 样本数 | 特征数 | 聚类数 | 来源 |
---|---|---|---|---|
Olive Oil | 30 | 570 | 4 | TSC |
SCADI | 70 | 205 | 7 | UCI |
SAP | 131 | 21 | 3 | UCI |
Divorce | 170 | 54 | 2 | UCI |
Sonar | 208 | 60 | 2 | UCI |
Ionosphere | 351 | 33 | 2 | UCI |
Wdbc | 569 | 30 | 2 | UCI |
German | 1 000 | 20 | 2 | UCI |
Waveform | 5 000 | 21 | 3 | UCI |
数据集 | UDFRFS | UFRFS | FRUAR | SUIFR | |||
---|---|---|---|---|---|---|---|
运行时间/s | 运行时间/s | TRR/% | 运行时间/s | TRR/% | 运行时间/s | TRR/% | |
Olive Oil | 0.001 1 | 0.496 6 | 0.21 | 7.739 2 | 0.01 | 50.603 8 | 0.00 |
SCADI | 0.005 5 | 0.484 2 | 1.13 | 134.344 8 | 0.00 | 21.391 2 | 0.03 |
SAP | 0.001 8 | 0.035 7 | 5.07 | 1.457 5 | 0.12 | 1.009 1 | 0.18 |
Divorce | 0.004 1 | 0.186 5 | 2.19 | 17.875 9 | 0.02 | 10.665 2 | 0.04 |
Sonar | 0.005 7 | 0.294 4 | 1.92 | 11.802 5 | 0.05 | 48.826 6 | 0.01 |
Ionosphere | 0.022 1 | 0.300 4 | 7.37 | 32.265 6 | 0.07 | 111.249 9 | 0.02 |
Wdbc | 0.039 4 | 0.704 8 | 5.60 | 25.644 7 | 0.15 | 98.890 0 | 0.04 |
German | 0.115 0 | 1.749 1 | 6.57 | 98.004 1 | 0.12 | 58.844 5 | 0.20 |
Waveform | 4.086 7 | 58.758 3 | 6.96 | — | — | — | — |
表2 不同大小基础数据集上不同算法的运行时间对比
Tab. 2 Comparison of running time of different algorithms on basic datasets with different scales
数据集 | UDFRFS | UFRFS | FRUAR | SUIFR | |||
---|---|---|---|---|---|---|---|
运行时间/s | 运行时间/s | TRR/% | 运行时间/s | TRR/% | 运行时间/s | TRR/% | |
Olive Oil | 0.001 1 | 0.496 6 | 0.21 | 7.739 2 | 0.01 | 50.603 8 | 0.00 |
SCADI | 0.005 5 | 0.484 2 | 1.13 | 134.344 8 | 0.00 | 21.391 2 | 0.03 |
SAP | 0.001 8 | 0.035 7 | 5.07 | 1.457 5 | 0.12 | 1.009 1 | 0.18 |
Divorce | 0.004 1 | 0.186 5 | 2.19 | 17.875 9 | 0.02 | 10.665 2 | 0.04 |
Sonar | 0.005 7 | 0.294 4 | 1.92 | 11.802 5 | 0.05 | 48.826 6 | 0.01 |
Ionosphere | 0.022 1 | 0.300 4 | 7.37 | 32.265 6 | 0.07 | 111.249 9 | 0.02 |
Wdbc | 0.039 4 | 0.704 8 | 5.60 | 25.644 7 | 0.15 | 98.890 0 | 0.04 |
German | 0.115 0 | 1.749 1 | 6.57 | 98.004 1 | 0.12 | 58.844 5 | 0.20 |
Waveform | 4.086 7 | 58.758 3 | 6.96 | — | — | — | — |
数据集 | UDFRFS | UFRFS | FRUAR | SUIFR | |||
---|---|---|---|---|---|---|---|
运行时间/s | 运行时间/s | TRR/% | 运行时间/s | TRR/% | 运行时间/s | TRR/% | |
OliveOil | 0.003 1 | 0.852 2 | 0.37 | 12.747 0 | 0.02 | 84.319 0 | 0.00 |
SCADI | 0.011 2 | 0.855 2 | 1.31 | 230.457 1 | 0.00 | 37.209 8 | 0.03 |
SAP | 0.004 7 | 0.049 7 | 9.41 | 2.698 4 | 0.17 | 1.754 5 | 0.27 |
Divorce | 0.009 2 | 0.292 9 | 3.14 | 28.547 7 | 0.03 | 16.738 9 | 0.05 |
Sonar | 0.012 7 | 0.498 5 | 2.54 | 19.326 7 | 0.07 | 82.935 8 | 0.02 |
Ionosphere | 0.024 3 | 0.379 5 | 6.39 | 43.882 4 | 0.06 | 150.410 6 | 0.02 |
Wdbc | 0.060 9 | 0.844 2 | 7.22 | 32.565 8 | 0.19 | 109.570 9 | 0.06 |
German | 0.181 8 | 2.704 0 | 6.72 | 167.698 3 | 0.11 | 97.490 2 | 0.19 |
Waveform | 5.971 3 | 104.561 1 | 5.71 | — | — | — | — |
表3 不同大小增量数据集上不同算法的运行时间对比
Tab. 3 Comparison of running time of different algorithms on incremental datasets with different scales
数据集 | UDFRFS | UFRFS | FRUAR | SUIFR | |||
---|---|---|---|---|---|---|---|
运行时间/s | 运行时间/s | TRR/% | 运行时间/s | TRR/% | 运行时间/s | TRR/% | |
OliveOil | 0.003 1 | 0.852 2 | 0.37 | 12.747 0 | 0.02 | 84.319 0 | 0.00 |
SCADI | 0.011 2 | 0.855 2 | 1.31 | 230.457 1 | 0.00 | 37.209 8 | 0.03 |
SAP | 0.004 7 | 0.049 7 | 9.41 | 2.698 4 | 0.17 | 1.754 5 | 0.27 |
Divorce | 0.009 2 | 0.292 9 | 3.14 | 28.547 7 | 0.03 | 16.738 9 | 0.05 |
Sonar | 0.012 7 | 0.498 5 | 2.54 | 19.326 7 | 0.07 | 82.935 8 | 0.02 |
Ionosphere | 0.024 3 | 0.379 5 | 6.39 | 43.882 4 | 0.06 | 150.410 6 | 0.02 |
Wdbc | 0.060 9 | 0.844 2 | 7.22 | 32.565 8 | 0.19 | 109.570 9 | 0.06 |
German | 0.181 8 | 2.704 0 | 6.72 | 167.698 3 | 0.11 | 97.490 2 | 0.19 |
Waveform | 5.971 3 | 104.561 1 | 5.71 | — | — | — | — |
数据集 | UDFRFS | UFRFS | FRUAR | SUIFR | ||||
---|---|---|---|---|---|---|---|---|
基础 | 增量 | 基础 | 增量 | 基础 | 增量 | 基础 | 增量 | |
Olive Oil | 4.74 | 4.81 | 4.74 | 4.81 | 1.10 | 1.00 | 1.00 | 1.00 |
SCADI | 17.10 | 17.36 | 17.10 | 17.36 | 16.94 | 16.47 | 1.00 | 1.00 |
SAP | 7.77 | 9.17 | 7.77 | 9.17 | 7.88 | 9.77 | 1.00 | 1.00 |
Divorce | 9.89 | 10.23 | 9.89 | 10.23 | 10.03 | 10.47 | 1.00 | 1.00 |
Sonar | 8.14 | 8.14 | 8.14 | 8.14 | 4.76 | 3.91 | 3.06 | 3.00 |
Ionosphere | 10.06 | 11.10 | 10.06 | 11.10 | 12.00 | 14.15 | 12.48 | 14.62 |
Wdbc | 7.94 | 8.54 | 7.94 | 8.54 | 5.20 | 4.68 | 3.31 | 3.00 |
German | 9.46 | 11.08 | 9.46 | 11.08 | 9.47 | 11.15 | 1.00 | 1.00 |
Waveform | 9.22 | 11.98 | 9.22 | 11.98 | — | — |
表4 不同算法的特征子集大小对比
Tab. 4 Comparison of feature subset size of different algorithms
数据集 | UDFRFS | UFRFS | FRUAR | SUIFR | ||||
---|---|---|---|---|---|---|---|---|
基础 | 增量 | 基础 | 增量 | 基础 | 增量 | 基础 | 增量 | |
Olive Oil | 4.74 | 4.81 | 4.74 | 4.81 | 1.10 | 1.00 | 1.00 | 1.00 |
SCADI | 17.10 | 17.36 | 17.10 | 17.36 | 16.94 | 16.47 | 1.00 | 1.00 |
SAP | 7.77 | 9.17 | 7.77 | 9.17 | 7.88 | 9.77 | 1.00 | 1.00 |
Divorce | 9.89 | 10.23 | 9.89 | 10.23 | 10.03 | 10.47 | 1.00 | 1.00 |
Sonar | 8.14 | 8.14 | 8.14 | 8.14 | 4.76 | 3.91 | 3.06 | 3.00 |
Ionosphere | 10.06 | 11.10 | 10.06 | 11.10 | 12.00 | 14.15 | 12.48 | 14.62 |
Wdbc | 7.94 | 8.54 | 7.94 | 8.54 | 5.20 | 4.68 | 3.31 | 3.00 |
German | 9.46 | 11.08 | 9.46 | 11.08 | 9.47 | 11.15 | 1.00 | 1.00 |
Waveform | 9.22 | 11.98 | 9.22 | 11.98 | — | — |
数据集 | UDFRFS | UFRFS | FRUAR | SUIFR | RAW1 | |||||
---|---|---|---|---|---|---|---|---|---|---|
基础 | 增量 | 基础 | 增量 | 基础 | 增量 | 基础 | 增量 | 基础 | 增量 | |
Olive Oil | 71.9 | 74.3 | 71.9 | 74.3 | 42.6 | 44.2 | 52.7 | 42.8 | 83.3 | 83.3 |
SCADI | 73.7 | 74.4 | 73.7 | 74.4 | 77.1 | 76.2 | 45.1 | 45.0 | 80.8 | 80.8 |
SAP | 43.1 | 43.3 | 43.1 | 43.3 | 43.5 | 43.4 | 42.9 | 41.8 | 43.7 | 42.1 |
Divorce | 97.7 | 97.7 | 97.7 | 97.7 | 97.7 | 97.8 | 91.7 | 89.1 | 97.6 | 97.7 |
Sonar | 78.0 | 76.9 | 78.0 | 76.9 | 61.5 | 58.7 | 60.4 | 62.2 | 83.8 | 85.3 |
Ionosphere | 86.7 | 87.5 | 86.7 | 87.5 | 86.1 | 86.3 | 87.0 | 86.5 | 85.8 | 85.4 |
Wdbc | 94.3 | 94.6 | 94.3 | 94.6 | 85.9 | 83.6 | 76.0 | 72.0 | 95.3 | 96.2 |
German | 90.5 | 90.2 | 90.5 | 90.2 | 88.7 | 90.0 | 80.0 | 79.4 | 69.1 | 70.6 |
Waveform | 72.0 | 76.2 | 72.0 | 76.2 | — | — | 73.1 | 78.2 |
表5 不同算法在KNN下的分类精度对比 (%)
Tab. 5 Comparison of classification accuracy of different algorithms under KNN
数据集 | UDFRFS | UFRFS | FRUAR | SUIFR | RAW1 | |||||
---|---|---|---|---|---|---|---|---|---|---|
基础 | 增量 | 基础 | 增量 | 基础 | 增量 | 基础 | 增量 | 基础 | 增量 | |
Olive Oil | 71.9 | 74.3 | 71.9 | 74.3 | 42.6 | 44.2 | 52.7 | 42.8 | 83.3 | 83.3 |
SCADI | 73.7 | 74.4 | 73.7 | 74.4 | 77.1 | 76.2 | 45.1 | 45.0 | 80.8 | 80.8 |
SAP | 43.1 | 43.3 | 43.1 | 43.3 | 43.5 | 43.4 | 42.9 | 41.8 | 43.7 | 42.1 |
Divorce | 97.7 | 97.7 | 97.7 | 97.7 | 97.7 | 97.8 | 91.7 | 89.1 | 97.6 | 97.7 |
Sonar | 78.0 | 76.9 | 78.0 | 76.9 | 61.5 | 58.7 | 60.4 | 62.2 | 83.8 | 85.3 |
Ionosphere | 86.7 | 87.5 | 86.7 | 87.5 | 86.1 | 86.3 | 87.0 | 86.5 | 85.8 | 85.4 |
Wdbc | 94.3 | 94.6 | 94.3 | 94.6 | 85.9 | 83.6 | 76.0 | 72.0 | 95.3 | 96.2 |
German | 90.5 | 90.2 | 90.5 | 90.2 | 88.7 | 90.0 | 80.0 | 79.4 | 69.1 | 70.6 |
Waveform | 72.0 | 76.2 | 72.0 | 76.2 | — | — | 73.1 | 78.2 |
数据集 | UDFRFS | UFRFS | FRUAR | SUIFR | RAW1 | |||||
---|---|---|---|---|---|---|---|---|---|---|
基础 | 增量 | 基础 | 增量 | 基础 | 增量 | 基础 | 增量 | 基础 | 增量 | |
Olive Oil | 73.3 | 76.7 | 73.3 | 76.7 | 45.1 | 47.1 | 56.6 | 52.1 | 24.1 | 4.3 |
SCADI | 74.3 | 75.5 | 74.3 | 75.5 | 78.1 | 77.0 | 44.8 | 44.3 | 81.5 | 82.9 |
SAP | 45.8 | 45.2 | 45.8 | 45.2 | 45.6 | 45.5 | 40.9 | 39.0 | 46.5 | 47.7 |
Divorce | 97.7 | 97.8 | 97.7 | 97.8 | 97.5 | 97.5 | 91.5 | 88.8 | 97.5 | 97.2 |
Sonar | 67.3 | 65.5 | 67.3 | 65.5 | 59.2 | 58.7 | 60.7 | 60.9 | 67.7 | 68.5 |
Ionosphere | 81.0 | 82.3 | 81.0 | 82.3 | 81.9 | 83.3 | 84.9 | 84.2 | 81.3 | 81.9 |
Wdbc | 92.6 | 92.6 | 92.6 | 92.6 | 82.9 | 80.2 | 75.6 | 70.3 | 92.7 | 93.1 |
German | 87.7 | 88.6 | 87.7 | 88.6 | 85.9 | 88.2 | 77.6 | 79.3 | 72.2 | 73.1 |
Waveform | 74.3 | 78.2 | 74.3 | 78.2 | — | — | — | — | 75.5 | 79.8 |
表6 不同算法在NB下的分类精度对比 (%)
Tab. 6 Comparison of classification accuracy of different algorithms under NB
数据集 | UDFRFS | UFRFS | FRUAR | SUIFR | RAW1 | |||||
---|---|---|---|---|---|---|---|---|---|---|
基础 | 增量 | 基础 | 增量 | 基础 | 增量 | 基础 | 增量 | 基础 | 增量 | |
Olive Oil | 73.3 | 76.7 | 73.3 | 76.7 | 45.1 | 47.1 | 56.6 | 52.1 | 24.1 | 4.3 |
SCADI | 74.3 | 75.5 | 74.3 | 75.5 | 78.1 | 77.0 | 44.8 | 44.3 | 81.5 | 82.9 |
SAP | 45.8 | 45.2 | 45.8 | 45.2 | 45.6 | 45.5 | 40.9 | 39.0 | 46.5 | 47.7 |
Divorce | 97.7 | 97.8 | 97.7 | 97.8 | 97.5 | 97.5 | 91.5 | 88.8 | 97.5 | 97.2 |
Sonar | 67.3 | 65.5 | 67.3 | 65.5 | 59.2 | 58.7 | 60.7 | 60.9 | 67.7 | 68.5 |
Ionosphere | 81.0 | 82.3 | 81.0 | 82.3 | 81.9 | 83.3 | 84.9 | 84.2 | 81.3 | 81.9 |
Wdbc | 92.6 | 92.6 | 92.6 | 92.6 | 82.9 | 80.2 | 75.6 | 70.3 | 92.7 | 93.1 |
German | 87.7 | 88.6 | 87.7 | 88.6 | 85.9 | 88.2 | 77.6 | 79.3 | 72.2 | 73.1 |
Waveform | 74.3 | 78.2 | 74.3 | 78.2 | — | — | — | — | 75.5 | 79.8 |
数据集 | UDFRFS | UFRFS | FRUAR | SUIFR | RAW1 | |||||
---|---|---|---|---|---|---|---|---|---|---|
基础 | 增量 | 基础 | 增量 | 基础 | 增量 | 基础 | 增量 | 基础 | 增量 | |
Olive Oil | 59.7 | 62.4 | 59.7 | 62.4 | 54.4 | 54.7 | 54.7 | 46.0 | 68.3 | 72.3 |
SCADI | 70.8 | 72.9 | 70.8 | 72.9 | 78.8 | 79.9 | 44.8 | 44.6 | 78.8 | 79.9 |
SAP | 43.6 | 42.8 | 43.6 | 42.8 | 44.0 | 42.6 | 43.6 | 43.8 | 43.2 | 41.1 |
Divorce | 95.0 | 95.2 | 95.0 | 95.2 | 94.7 | 94.5 | 91.4 | 89.0 | 96.3 | 96.7 |
Sonar | 70.0 | 68.0 | 70.0 | 68.0 | 62.5 | 63.0 | 62.8 | 63.8 | 72.9 | 74.4 |
Ionosphere | 86.8 | 87.5 | 86.8 | 87.5 | 87.2 | 87.9 | 89.0 | 88.9 | 87.9 | 89.1 |
Wdbc | 92.3 | 92.8 | 92.3 | 92.8 | 85.1 | 84.4 | 76.3 | 71.6 | 92.5 | 93.1 |
German | 95.2 | 96.6 | 95.2 | 96.6 | 93.7 | 96.6 | 80.1 | 79.4 | 71.1 | 72.2 |
Waveform | 72.0 | 74.7 | 72.0 | 74.7 | — | — | — | — | 72.6 | 76.1 |
表7 不同算法在CART下的分类精度对比 (%)
Tab. 7 Comparison of classification accuracy of different algorithms under CART
数据集 | UDFRFS | UFRFS | FRUAR | SUIFR | RAW1 | |||||
---|---|---|---|---|---|---|---|---|---|---|
基础 | 增量 | 基础 | 增量 | 基础 | 增量 | 基础 | 增量 | 基础 | 增量 | |
Olive Oil | 59.7 | 62.4 | 59.7 | 62.4 | 54.4 | 54.7 | 54.7 | 46.0 | 68.3 | 72.3 |
SCADI | 70.8 | 72.9 | 70.8 | 72.9 | 78.8 | 79.9 | 44.8 | 44.6 | 78.8 | 79.9 |
SAP | 43.6 | 42.8 | 43.6 | 42.8 | 44.0 | 42.6 | 43.6 | 43.8 | 43.2 | 41.1 |
Divorce | 95.0 | 95.2 | 95.0 | 95.2 | 94.7 | 94.5 | 91.4 | 89.0 | 96.3 | 96.7 |
Sonar | 70.0 | 68.0 | 70.0 | 68.0 | 62.5 | 63.0 | 62.8 | 63.8 | 72.9 | 74.4 |
Ionosphere | 86.8 | 87.5 | 86.8 | 87.5 | 87.2 | 87.9 | 89.0 | 88.9 | 87.9 | 89.1 |
Wdbc | 92.3 | 92.8 | 92.3 | 92.8 | 85.1 | 84.4 | 76.3 | 71.6 | 92.5 | 93.1 |
German | 95.2 | 96.6 | 95.2 | 96.6 | 93.7 | 96.6 | 80.1 | 79.4 | 71.1 | 72.2 |
Waveform | 72.0 | 74.7 | 72.0 | 74.7 | — | — | — | — | 72.6 | 76.1 |
数据集 | UDFRFS | UFRFS | FRUAR | SUIFR | RAW2 | |||||
---|---|---|---|---|---|---|---|---|---|---|
基础 | 增量 | 基础 | 增量 | 基础 | 增量 | 基础 | 增量 | 基础 | 增量 | |
Olive Oil | 17.2 | 17.5 | 17.3 | 17.5 | 4.2 | 4.0 | 3.2 | 2.8 | 1 178.8 | 1 652.5 |
SCADI | -2.5E+9 | -1.1E+9 | -2.5E+9 | -1.1E+9 | -1.1E+9 | -3.6E+9 | 6.4 | -4.0E+8 | -3.1E+10 | -4.6E+10 |
SAP | -9.5 | -11.4 | -9.5 | -11.4 | -10.0 | -12.9 | -0.4 | -1.4 | -6.2 | -8.7 |
Divorce | -12.5 | -12.7 | -12.5 | -12.7 | -13.0 | -13.7 | -1.5 | -1.5 | -31.8 | -42.5 |
Sonar | 7.3 | 7.7 | 7.3 | 7.7 | 12.1 | 12.5 | 7.6 | 7.1 | 35.8 | 52.1 |
Ionosphere | -6.4 | -7.0 | -6.4 | -7.0 | -7.6 | -8.9 | -7.7 | -9.2 | -9.8 | -12.1 |
Wdbc | 2.5 | 6.1 | 2.5 | 6.1 | 10.6 | 15.1 | 9.9 | 11.4 | 1.3 | 3.6 |
German | -21.4 | -27.1 | -21.4 | -27.1 | -21.5 | -27.8 | -1.5 | -1.1 | -19.6 | -28.4 |
Waveform | -15.1 | -19.4 | -15.1 | -19.4 | — | — | — | — | -17.8 | -24.8 |
表8 不同算法在各数据集上的对数似然对比
Tab. 8 Comparison of log likelihood of different algorithms under different datasets
数据集 | UDFRFS | UFRFS | FRUAR | SUIFR | RAW2 | |||||
---|---|---|---|---|---|---|---|---|---|---|
基础 | 增量 | 基础 | 增量 | 基础 | 增量 | 基础 | 增量 | 基础 | 增量 | |
Olive Oil | 17.2 | 17.5 | 17.3 | 17.5 | 4.2 | 4.0 | 3.2 | 2.8 | 1 178.8 | 1 652.5 |
SCADI | -2.5E+9 | -1.1E+9 | -2.5E+9 | -1.1E+9 | -1.1E+9 | -3.6E+9 | 6.4 | -4.0E+8 | -3.1E+10 | -4.6E+10 |
SAP | -9.5 | -11.4 | -9.5 | -11.4 | -10.0 | -12.9 | -0.4 | -1.4 | -6.2 | -8.7 |
Divorce | -12.5 | -12.7 | -12.5 | -12.7 | -13.0 | -13.7 | -1.5 | -1.5 | -31.8 | -42.5 |
Sonar | 7.3 | 7.7 | 7.3 | 7.7 | 12.1 | 12.5 | 7.6 | 7.1 | 35.8 | 52.1 |
Ionosphere | -6.4 | -7.0 | -6.4 | -7.0 | -7.6 | -8.9 | -7.7 | -9.2 | -9.8 | -12.1 |
Wdbc | 2.5 | 6.1 | 2.5 | 6.1 | 10.6 | 15.1 | 9.9 | 11.4 | 1.3 | 3.6 |
German | -21.4 | -27.1 | -21.4 | -27.1 | -21.5 | -27.8 | -1.5 | -1.1 | -19.6 | -28.4 |
Waveform | -15.1 | -19.4 | -15.1 | -19.4 | — | — | — | — | -17.8 | -24.8 |
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