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How convolutional neural networks see the world --- A survey of convolutional neural network visualization methods
Hybrid binary dragonfly enhanced particle swarm optimization algorithm for solving feature selection problems
1. | Department of Mathematics and Statistics, Faculty of Science, Thompson Rivers University, Kamloops, BC, V2C 0C8, Canada |
2. | Department of Mathematics and Computer Science, Faculty of Science, Alexandria University, Moharam Bey 21511, Alexandria, Egyp |
3. | Electrical and Computer Engineering, The University of British Columbia, Vancouver BC V6T 1Z4, Canada |
In this paper, we present a new hybrid binary version of dragonfly and enhanced particle swarm optimization algorithm in order to solve feature selection problems. The proposed algorithm is called Hybrid Binary Dragonfly Enhanced Particle Swarm Optimization Algorithm(HBDESPO). In the proposed HBDESPO algorithm, we combine the dragonfly algorithm with its ability to encourage diverse solutions with its formation of static swarms and the enhanced version of the particle swarm optimization exploiting the data with its ability to converge to the best global solution in the search space. In order to investigate the general performance of the proposed HBDESPO algorithm, the proposed algorithm is compared with the original optimizers and other optimizers that have been used for feature selection in the past. Further, we use a set of assessment indicators to evaluate and compare the different optimizers over 20 standard data sets obtained from the UCI repository. Results prove the ability of the proposed HBDESPO algorithm to search the feature space for optimal feature combinations.
References:
[1] |
D. K. Agrafiotis and W. Cedeno, Feature selection for structure-activity correlation using binary particle swarms, Journal of Medicinal Chemistry, 45 (2002), 1098-1107. Google Scholar |
[2] |
H. Banati and M. Bajaj, Fire fly based feature selection approach, IJCSI International Journal of Computer Science Issues, 8 (2011). Google Scholar |
[3] |
D. Bell and H. Wang, A formalism for relevance and its application in feature subset selection, Mach. Learn., 41 (2000), 175-195. Google Scholar |
[4] |
B. Xue, M. Zhang, W. Browne and X. Yao,
A survey on evolutionary computation approaches to feature selection, IEEE Transaction on Evolutionary Computation, 20 (2016), 606-626.
doi: 10.1109/TEVC.2015.2504420. |
[5] |
G. Chandrashekar and F. Sahin, A survey on feature selection methods, Electrical and Microelectronic Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA, 2013. Google Scholar |
[6] |
B. Chizi, L. Rokach and O. Maimon, A survey of feature selection techniques, Encyclopedia of Data Warehousing and Mining, seconded, IGI Global, (2009), 1888-1895. Google Scholar |
[7] |
L. Y. Chuang, H. W. Chang, C. J. Tu and C. H. Yang, Improved binary PSO for feature selection using gene expression data, Comput.Biol.Chem., 32 (2008), 29-38. Google Scholar |
[8] |
G. Coath and S. K. Halgamuge, A comparison of constraint-handling methods for the application of particle swarm optimization to constrained nonlinear optimization problems, Proceedings of IEEE Congress on Evolutionary Computation 2003 (CEC 2003), Canbella, Australia, (2003), 2419–2425. Google Scholar |
[9] |
C. A. Coello Coello, E. H. Luna and A. H. Aguirre,
Use of particle swarm optimization to design combinational logic circuits, International Conference on Evolvable Systems, (2003), 398-409.
doi: 10.1007/3-540-36553-2_36. |
[10] |
C. Cotta, A study of hybridisation techniques and their application to the design of evolutionary algorithms, AI Communications, 11 (1998), 223-224. Google Scholar |
[11] |
R. C. Eberhart and J. Kennedy, A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan, (1995), 39–43. Google Scholar |
[12] |
E. Emary, H. M. Zawbaa, C. Grosan and A. E. Hassanien, Binary grey wolf optimization approaches for feature selection, Neurocomputing, Elsevier, 172 (2016), 371-381. Google Scholar |
[13] |
A. Frank and A. Asuncion, UCI Machine Learning Repository, 2010. Google Scholar |
[14] |
J. Huang, Y. Cai and X. Xu,
A hybrid genetic algorithm for feature selection wrapper based on mutual information, Pattern Recognition Letters archive, 28 (2007), 1825-1844.
doi: 10.1016/j.patrec.2007.05.011. |
[15] |
J. Kennedy, R. C. Eberhart and Y. Shi, Swarm Intelligence, Morgan Kaufmann, SanMateo, CA, 2001. Google Scholar |
[16] |
S. Khalid, A survey of feature selection and feature extraction techniques in machine learning, Science and Information Conference (SAI), 2014. Google Scholar |
[17] |
R. A. Krohling, H. Knidel and Y. Shi, Solving numerical equations of hydraulic problems using particle swarm optimization, Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2002), Honolulu, Hawaii USA, 2002. Google Scholar |
[18] |
S. Mirjalili and A. Lewis, S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization, Swarm and Evolutionary Computation, 9 (2012), 1-14. Google Scholar |
[19] |
S. Mirjalili, Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems, Neural Computing and Applications, 27 (2016), 1053-1073. Google Scholar |
[20] |
R. Y. M. Nakamura, L. A. M. Pereira, K. A. Costa, D. Rodrigues, J. P. Papa and X.-S. Yang, Binary bat algorithm for feature selection, Conference on Graphics, Patterns and Images, (2012), 291-297. Google Scholar |
[21] |
Q. Gu, Z. Li and J. Han, Generalized Fisher Score for Feature Selection, In Proc. of the 27th Conference on Uncertainty in Artificial Intelligence (UAI), Barcelona, Spain, 2011. Google Scholar |
[22] |
E. G. Talbi, A taxonomy of hybrid metaheuristics, Journal of Heuristics, 8 (2002), 541-565. Google Scholar |
[23] |
D. Wolpert and W. Macready, No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, 1 (1997), 67-72. Google Scholar |
show all references
References:
[1] |
D. K. Agrafiotis and W. Cedeno, Feature selection for structure-activity correlation using binary particle swarms, Journal of Medicinal Chemistry, 45 (2002), 1098-1107. Google Scholar |
[2] |
H. Banati and M. Bajaj, Fire fly based feature selection approach, IJCSI International Journal of Computer Science Issues, 8 (2011). Google Scholar |
[3] |
D. Bell and H. Wang, A formalism for relevance and its application in feature subset selection, Mach. Learn., 41 (2000), 175-195. Google Scholar |
[4] |
B. Xue, M. Zhang, W. Browne and X. Yao,
A survey on evolutionary computation approaches to feature selection, IEEE Transaction on Evolutionary Computation, 20 (2016), 606-626.
doi: 10.1109/TEVC.2015.2504420. |
[5] |
G. Chandrashekar and F. Sahin, A survey on feature selection methods, Electrical and Microelectronic Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA, 2013. Google Scholar |
[6] |
B. Chizi, L. Rokach and O. Maimon, A survey of feature selection techniques, Encyclopedia of Data Warehousing and Mining, seconded, IGI Global, (2009), 1888-1895. Google Scholar |
[7] |
L. Y. Chuang, H. W. Chang, C. J. Tu and C. H. Yang, Improved binary PSO for feature selection using gene expression data, Comput.Biol.Chem., 32 (2008), 29-38. Google Scholar |
[8] |
G. Coath and S. K. Halgamuge, A comparison of constraint-handling methods for the application of particle swarm optimization to constrained nonlinear optimization problems, Proceedings of IEEE Congress on Evolutionary Computation 2003 (CEC 2003), Canbella, Australia, (2003), 2419–2425. Google Scholar |
[9] |
C. A. Coello Coello, E. H. Luna and A. H. Aguirre,
Use of particle swarm optimization to design combinational logic circuits, International Conference on Evolvable Systems, (2003), 398-409.
doi: 10.1007/3-540-36553-2_36. |
[10] |
C. Cotta, A study of hybridisation techniques and their application to the design of evolutionary algorithms, AI Communications, 11 (1998), 223-224. Google Scholar |
[11] |
R. C. Eberhart and J. Kennedy, A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan, (1995), 39–43. Google Scholar |
[12] |
E. Emary, H. M. Zawbaa, C. Grosan and A. E. Hassanien, Binary grey wolf optimization approaches for feature selection, Neurocomputing, Elsevier, 172 (2016), 371-381. Google Scholar |
[13] |
A. Frank and A. Asuncion, UCI Machine Learning Repository, 2010. Google Scholar |
[14] |
J. Huang, Y. Cai and X. Xu,
A hybrid genetic algorithm for feature selection wrapper based on mutual information, Pattern Recognition Letters archive, 28 (2007), 1825-1844.
doi: 10.1016/j.patrec.2007.05.011. |
[15] |
J. Kennedy, R. C. Eberhart and Y. Shi, Swarm Intelligence, Morgan Kaufmann, SanMateo, CA, 2001. Google Scholar |
[16] |
S. Khalid, A survey of feature selection and feature extraction techniques in machine learning, Science and Information Conference (SAI), 2014. Google Scholar |
[17] |
R. A. Krohling, H. Knidel and Y. Shi, Solving numerical equations of hydraulic problems using particle swarm optimization, Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2002), Honolulu, Hawaii USA, 2002. Google Scholar |
[18] |
S. Mirjalili and A. Lewis, S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization, Swarm and Evolutionary Computation, 9 (2012), 1-14. Google Scholar |
[19] |
S. Mirjalili, Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems, Neural Computing and Applications, 27 (2016), 1053-1073. Google Scholar |
[20] |
R. Y. M. Nakamura, L. A. M. Pereira, K. A. Costa, D. Rodrigues, J. P. Papa and X.-S. Yang, Binary bat algorithm for feature selection, Conference on Graphics, Patterns and Images, (2012), 291-297. Google Scholar |
[21] |
Q. Gu, Z. Li and J. Han, Generalized Fisher Score for Feature Selection, In Proc. of the 27th Conference on Uncertainty in Artificial Intelligence (UAI), Barcelona, Spain, 2011. Google Scholar |
[22] |
E. G. Talbi, A taxonomy of hybrid metaheuristics, Journal of Heuristics, 8 (2002), 541-565. Google Scholar |
[23] |
D. Wolpert and W. Macready, No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, 1 (1997), 67-72. Google Scholar |


Dataset | # of Attributes | # of Instances |
Zoo | 16 | 101 |
WineEW | 13 | 178 |
IonosphereEW | 34 | 351 |
WaveformEW | 40 | 5000 |
BreastEW | 30 | 569 |
Breastcancer | 9 | 699 |
Congress | 16 | 435 |
Exactly | 13 | 1000 |
Exactly2 | 13 | 1000 |
HeartEW | 13 | 270 |
KrvskpEW | 36 | 3196 |
M-of-n | 13 | 1000 |
SonarEW | 60 | 208 |
SpectEW | 60 | 208 |
Tic-tac-toe | 9 | 958 |
Lymphography | 18 | 148 |
Dermatology | 34 | 366 |
Echocardiogram | 12 | 132 |
hepatitis | 19 | 155 |
LungCancer | 56 | 32 |
Dataset | # of Attributes | # of Instances |
Zoo | 16 | 101 |
WineEW | 13 | 178 |
IonosphereEW | 34 | 351 |
WaveformEW | 40 | 5000 |
BreastEW | 30 | 569 |
Breastcancer | 9 | 699 |
Congress | 16 | 435 |
Exactly | 13 | 1000 |
Exactly2 | 13 | 1000 |
HeartEW | 13 | 270 |
KrvskpEW | 36 | 3196 |
M-of-n | 13 | 1000 |
SonarEW | 60 | 208 |
SpectEW | 60 | 208 |
Tic-tac-toe | 9 | 958 |
Lymphography | 18 | 148 |
Dermatology | 34 | 366 |
Echocardiogram | 12 | 132 |
hepatitis | 19 | 155 |
LungCancer | 56 | 32 |
Parameter | Value |
No of iterations( | 70 |
No of search agents( | 5 |
Dimension( | No. of features in the data |
Search domain | [0 1] |
No of runs( | 10 |
| 0.9 |
| 0.4 |
| 6 |
| 2 |
| 2 |
| 6 |
| 0.01 |
| 0.99 |
Parameter | Value |
No of iterations( | 70 |
No of search agents( | 5 |
Dimension( | No. of features in the data |
Search domain | [0 1] |
No of runs( | 10 |
| 0.9 |
| 0.4 |
| 6 |
| 2 |
| 2 |
| 6 |
| 0.01 |
| 0.99 |
Dataset | HBDESPO | BDA | EPSO | BGA | BBA | BGWO2 | HBEPSOD |
Zoo | 0.040 | 0.067 | 0.031 | 0.124 | 0.094 | 0.119 | 0.082 |
Wine EW | 0.036 | 0.050 | 0.042 | 0.065 | 0.128 | 0.092 | 0.041 |
IonosphereEW | 0.110 | 0.130 | 0.137 | 0.143 | 0.146 | 0.172 | 0.115 |
WaveformEW | 0.179 | 0.183 | 0.175 | 0.186 | 0.193 | 0.185 | 0.175 |
BreastEW | 0.040 | 0.057 | 0.050 | 0.106 | 0.070 | 0.080 | 0.044 |
Breastcancer | 0.023 | 0.032 | 0.032 | 0.036 | 0.035 | 0.042 | 0.030 |
Congress | 0.028 | 0.042 | 0.033 | 0.059 | 0.053 | 0.073 | 0.036 |
Exactly | 0.103 | 0.178 | 0.104 | 0.269 | 0.303 | 0.316 | 0.139 |
Exactly2 | 0.224 | 0.240 | 0.234 | 0.243 | 0.243 | 0.263 | 0.241 |
HeartEW | 0.125 | 0.153 | 0.153 | 0.250 | 0.240 | 0.268 | 0.128 |
KrvskpEW | 0.044 | 0.041 | 0.043 | 0.089 | 0.108 | 0.080 | 0.039 |
M-of-n | 0.025 | 0.048 | 0.024 | 0.108 | 0.167 | 0.154 | 0.084 |
SonarEW | 0.158 | 0.194 | 0.192 | 0.262 | 0.277 | 0.290 | 0.179 |
SpectEW | 0.148 | 0.133 | 0.160 | 0.168 | 0.167 | 0.205 | 0.142 |
Tic-tac-toe | 0.222 | 0.223 | 0.222 | 0.241 | 0.270 | 0.262 | 0.227 |
Lymphography | 0.381 | 0.392 | 0.412 | 0.466 | 0.487 | 0.531 | 0.426 |
Dermatology | 0.016 | 0.017 | 0.016 | 0.031 | 0.081 | 0.099 | 0.017 |
Echocardiogram | 0.051 | 0.058 | 0.083 | 0.072 | 0.112 | 0.200 | 0.074 |
Hepatitis | 0.118 | 0.101 | 0.123 | 0.152 | 0.175 | 0.192 | 0.115 |
LungCancer | 0.219 | 0.255 | 0.220 | 0.318 | 0.427 | 0.455 | 0.291 |
Average | 0.114 | 0.131 | 0.123 | 0.169 | 0.189 | 0.204 | 0.131 |
Dataset | HBDESPO | BDA | EPSO | BGA | BBA | BGWO2 | HBEPSOD |
Zoo | 0.040 | 0.067 | 0.031 | 0.124 | 0.094 | 0.119 | 0.082 |
Wine EW | 0.036 | 0.050 | 0.042 | 0.065 | 0.128 | 0.092 | 0.041 |
IonosphereEW | 0.110 | 0.130 | 0.137 | 0.143 | 0.146 | 0.172 | 0.115 |
WaveformEW | 0.179 | 0.183 | 0.175 | 0.186 | 0.193 | 0.185 | 0.175 |
BreastEW | 0.040 | 0.057 | 0.050 | 0.106 | 0.070 | 0.080 | 0.044 |
Breastcancer | 0.023 | 0.032 | 0.032 | 0.036 | 0.035 | 0.042 | 0.030 |
Congress | 0.028 | 0.042 | 0.033 | 0.059 | 0.053 | 0.073 | 0.036 |
Exactly | 0.103 | 0.178 | 0.104 | 0.269 | 0.303 | 0.316 | 0.139 |
Exactly2 | 0.224 | 0.240 | 0.234 | 0.243 | 0.243 | 0.263 | 0.241 |
HeartEW | 0.125 | 0.153 | 0.153 | 0.250 | 0.240 | 0.268 | 0.128 |
KrvskpEW | 0.044 | 0.041 | 0.043 | 0.089 | 0.108 | 0.080 | 0.039 |
M-of-n | 0.025 | 0.048 | 0.024 | 0.108 | 0.167 | 0.154 | 0.084 |
SonarEW | 0.158 | 0.194 | 0.192 | 0.262 | 0.277 | 0.290 | 0.179 |
SpectEW | 0.148 | 0.133 | 0.160 | 0.168 | 0.167 | 0.205 | 0.142 |
Tic-tac-toe | 0.222 | 0.223 | 0.222 | 0.241 | 0.270 | 0.262 | 0.227 |
Lymphography | 0.381 | 0.392 | 0.412 | 0.466 | 0.487 | 0.531 | 0.426 |
Dermatology | 0.016 | 0.017 | 0.016 | 0.031 | 0.081 | 0.099 | 0.017 |
Echocardiogram | 0.051 | 0.058 | 0.083 | 0.072 | 0.112 | 0.200 | 0.074 |
Hepatitis | 0.118 | 0.101 | 0.123 | 0.152 | 0.175 | 0.192 | 0.115 |
LungCancer | 0.219 | 0.255 | 0.220 | 0.318 | 0.427 | 0.455 | 0.291 |
Average | 0.114 | 0.131 | 0.123 | 0.169 | 0.189 | 0.204 | 0.131 |
Dataset | HBDESPO | BDA | EPSO | BGA | BBA | BGWO2 | HBEPSOD |
Zoo | 0.000 | 0.000 | 0.001 | 0.032 | 0.005 | 0.035 | 0.004 |
Wine EW | 0.002 | 0.003 | 0.019 | 0.035 | 0.021 | 0.003 | 0.019 |
IonosphereEW | 0.071 | 0.108 | 0.113 | 0.114 | 0.079 | 0.089 | 0.096 |
WaveformEW | 0.171 | 0.181 | 0.165 | 0.174 | 0.176 | 0.167 | 0.162 |
BreastEW | 0.025 | 0.055 | 0.027 | 0.060 | 0.045 | 0.056 | 0.034 |
Breastcancer | 0.014 | 0.024 | 0.018 | 0.029 | 0.024 | 0.027 | 0.014 |
Congress | 0.016 | 0.019 | 0.022 | 0.038 | 0.029 | 0.045 | 0.022 |
Exactly | 0.004 | 0.040 | 0.025 | 0.058 | 0.270 | 0.298 | 0.025 |
Exactly2 | 0.211 | 0.235 | 0.219 | 0.216 | 0.212 | 0.241 | 0.220 |
HeartEW | 0.091 | 0.082 | 0.104 | 0.147 | 0.168 | 0.147 | 0.082 |
KrvskpEW | 0.041 | 0.034 | 0.033 | 0.041 | 0.060 | 0.059 | 0.029 |
M-of-n | 0.004 | 0.004 | 0.004 | 0.067 | 0.113 | 0.128 | 0.004 |
SonarEW | 0.118 | 0.156 | 0.118 | 0.220 | 0.205 | 0.234 | 0.134 |
SpectEW | 0.115 | 0.093 | 0.125 | 0.125 | 0.127 | 0.161 | 0.115 |
Tic-tac-toe | 0.213 | 0.206 | 0.185 | 0.217 | 0.236 | 0.242 | 0.196 |
Lymphography | 0.286 | 0.344 | 0.307 | 0.388 | 0.427 | 0.450 | 0.349 |
Dermatology | 0.003 | 0.003 | 0.004 | 0.012 | 0.029 | 0.046 | 0.004 |
Echocardiogram | 0.003 | 0.025 | 0.047 | 0.045 | 0.049 | 0.093 | 0.047 |
Hepatitis | 0.058 | 0.058 | 0.080 | 0.078 | 0.117 | 0.097 | 0.061 |
LungCancer | 0.093 | 0.003 | 0.058 | 0.093 | 0.184 | 0.28 | 0.094 |
Average | 0.077 | 0.084 | 0.084 | 0.110 | 0.129 | 0.145 | 0.086 |
Dataset | HBDESPO | BDA | EPSO | BGA | BBA | BGWO2 | HBEPSOD |
Zoo | 0.000 | 0.000 | 0.001 | 0.032 | 0.005 | 0.035 | 0.004 |
Wine EW | 0.002 | 0.003 | 0.019 | 0.035 | 0.021 | 0.003 | 0.019 |
IonosphereEW | 0.071 | 0.108 | 0.113 | 0.114 | 0.079 | 0.089 | 0.096 |
WaveformEW | 0.171 | 0.181 | 0.165 | 0.174 | 0.176 | 0.167 | 0.162 |
BreastEW | 0.025 | 0.055 | 0.027 | 0.060 | 0.045 | 0.056 | 0.034 |
Breastcancer | 0.014 | 0.024 | 0.018 | 0.029 | 0.024 | 0.027 | 0.014 |
Congress | 0.016 | 0.019 | 0.022 | 0.038 | 0.029 | 0.045 | 0.022 |
Exactly | 0.004 | 0.040 | 0.025 | 0.058 | 0.270 | 0.298 | 0.025 |
Exactly2 | 0.211 | 0.235 | 0.219 | 0.216 | 0.212 | 0.241 | 0.220 |
HeartEW | 0.091 | 0.082 | 0.104 | 0.147 | 0.168 | 0.147 | 0.082 |
KrvskpEW | 0.041 | 0.034 | 0.033 | 0.041 | 0.060 | 0.059 | 0.029 |
M-of-n | 0.004 | 0.004 | 0.004 | 0.067 | 0.113 | 0.128 | 0.004 |
SonarEW | 0.118 | 0.156 | 0.118 | 0.220 | 0.205 | 0.234 | 0.134 |
SpectEW | 0.115 | 0.093 | 0.125 | 0.125 | 0.127 | 0.161 | 0.115 |
Tic-tac-toe | 0.213 | 0.206 | 0.185 | 0.217 | 0.236 | 0.242 | 0.196 |
Lymphography | 0.286 | 0.344 | 0.307 | 0.388 | 0.427 | 0.450 | 0.349 |
Dermatology | 0.003 | 0.003 | 0.004 | 0.012 | 0.029 | 0.046 | 0.004 |
Echocardiogram | 0.003 | 0.025 | 0.047 | 0.045 | 0.049 | 0.093 | 0.047 |
Hepatitis | 0.058 | 0.058 | 0.080 | 0.078 | 0.117 | 0.097 | 0.061 |
LungCancer | 0.093 | 0.003 | 0.058 | 0.093 | 0.184 | 0.28 | 0.094 |
Average | 0.077 | 0.084 | 0.084 | 0.110 | 0.129 | 0.145 | 0.086 |
Dataset | HBDESPO | BDA | EPSO | BGA | BBA | BGWO2 | HBEPSOD |
Zoo | 0.121 | 0.208 | 0.089 | 0.208 | 0.208 | 0.208 | 0.206 |
Wine EW | 0.069 | 0.119 | 0.070 | 0.122 | 0.273 | 0.157 | 0.070 |
IonosphereEW | 0.146 | 0.155 | 0.171 | 0.189 | 0.191 | 0.309 | 0.147 |
WaveformEW | 0.184 | 0.192 | 0.186 | 0.197 | 0.215 | 0.195 | 0.186 |
BreastEW | 0.049 | 0.065 | 0.081 | 0.315 | 0.103 | 0.115 | 0.054 |
Breastcancer | 0.031 | 0.039 | 0.041 | 0.049 | 0.049 | 0.052 | 0.038 |
Congress | 0.043 | 0.063 | 0.049 | 0.085 | 0.092 | 0.089 | 0.049 |
Exactly | 0.213 | 0.308 | 0.251 | 0.349 | 0.326 | 0.342 | 0.294 |
Exactly2 | 0.238 | 0.263 | 0.248 | 0.268 | 0.276 | 0.286 | 0.265 |
HeartEW | 0.168 | 0.201 | 0.289 | 0.322 | 0.334 | 0.357 | 0.168 |
KrvskpEW | 0.047 | 0.052 | 0.054 | 0.177 | 0.191 | 0.101 | 0.063 |
M-of-n | 0.049 | 0.136 | 0.073 | 0.157 | 0.232 | 0.170 | 0.461 |
SonarEW | 0.191 | 0.234 | 0.219 | 0.306 | 0.391 | 0.349 | 0.262 |
SpectEW | 0.170 | 0.170 | 0.204 | 0.205 | 0.216 | 0.238 | 0.192 |
Tic-tac-toe | 0.236 | 0.239 | 0.244 | 0.275 | 0.313 | 0.298 | 0.243 |
Lymphography | 0.468 | 0.491 | 0.469 | 0.588 | 0.569 | 0.581 | 0.549 |
Dermatology | 0.029 | 0.053 | 0.029 | 0.061 | 0.290 | 0.222 | 0.030 |
Echocardiogram | 0.070 | 0.092 | 0.16 | 0.114 | 0.23 | 0.840 | 0.115 |
Hepatitis | 0.230 | 0.138 | 0.174 | 0.212 | 0.234 | 0.253 | 0.175 |
LungCancer | 0.542 | 0.454 | 0.543 | 0.723 | 0.813 | 0.545 | 0.722 |
Average | 0.165 | 0.184 | 0.182 | 0.246 | 0.277 | 0.285 | 0.214 |
Dataset | HBDESPO | BDA | EPSO | BGA | BBA | BGWO2 | HBEPSOD |
Zoo | 0.121 | 0.208 | 0.089 | 0.208 | 0.208 | 0.208 | 0.206 |
Wine EW | 0.069 | 0.119 | 0.070 | 0.122 | 0.273 | 0.157 | 0.070 |
IonosphereEW | 0.146 | 0.155 | 0.171 | 0.189 | 0.191 | 0.309 | 0.147 |
WaveformEW | 0.184 | 0.192 | 0.186 | 0.197 | 0.215 | 0.195 | 0.186 |
BreastEW | 0.049 | 0.065 | 0.081 | 0.315 | 0.103 | 0.115 | 0.054 |
Breastcancer | 0.031 | 0.039 | 0.041 | 0.049 | 0.049 | 0.052 | 0.038 |
Congress | 0.043 | 0.063 | 0.049 | 0.085 | 0.092 | 0.089 | 0.049 |
Exactly | 0.213 | 0.308 | 0.251 | 0.349 | 0.326 | 0.342 | 0.294 |
Exactly2 | 0.238 | 0.263 | 0.248 | 0.268 | 0.276 | 0.286 | 0.265 |
HeartEW | 0.168 | 0.201 | 0.289 | 0.322 | 0.334 | 0.357 | 0.168 |
KrvskpEW | 0.047 | 0.052 | 0.054 | 0.177 | 0.191 | 0.101 | 0.063 |
M-of-n | 0.049 | 0.136 | 0.073 | 0.157 | 0.232 | 0.170 | 0.461 |
SonarEW | 0.191 | 0.234 | 0.219 | 0.306 | 0.391 | 0.349 | 0.262 |
SpectEW | 0.170 | 0.170 | 0.204 | 0.205 | 0.216 | 0.238 | 0.192 |
Tic-tac-toe | 0.236 | 0.239 | 0.244 | 0.275 | 0.313 | 0.298 | 0.243 |
Lymphography | 0.468 | 0.491 | 0.469 | 0.588 | 0.569 | 0.581 | 0.549 |
Dermatology | 0.029 | 0.053 | 0.029 | 0.061 | 0.290 | 0.222 | 0.030 |
Echocardiogram | 0.070 | 0.092 | 0.16 | 0.114 | 0.23 | 0.840 | 0.115 |
Hepatitis | 0.230 | 0.138 | 0.174 | 0.212 | 0.234 | 0.253 | 0.175 |
LungCancer | 0.542 | 0.454 | 0.543 | 0.723 | 0.813 | 0.545 | 0.722 |
Average | 0.165 | 0.184 | 0.182 | 0.246 | 0.277 | 0.285 | 0.214 |
Dataset | HBDESPO | BDA | EPSO | BGA | BBA | BGWO2 | HBEPSOD |
Zoo | 0.052 | 0.075 | 0.033 | 0.066 | 0.070 | 0.067 | 0.056 |
Wine EW | 0.019 | 0.030 | 0.018 | 0.026 | 0.080 | 0.057 | 0.017 |
IonosphereEW | 0.022 | 0.018 | 0.016 | 0.025 | 0.040 | 0.057 | 0.013 |
WaveformEW | 0.003 | 0.006 | 0.008 | 0.008 | 0.0123 | 0.007 | 0.006 |
BreastEW | 0.006 | 0.007 | 0.019 | 0.755 | 0.017 | 0.018 | 0.006 |
Breastcancer | 0.005 | 0.005 | 0.007 | 0.007 | 0.009 | 0.008 | 0.009 |
Congress | 0.007 | 0.016 | 0.008 | 0.013 | 0.019 | 0.015 | 0.008 |
Exactly | 0.071 | 0.119 | 0.082 | 0.078 | 0.020 | 0.016 | 0.117 |
Exactly2 | 0.009 | 0.015 | 0.009 | 0.019 | 0.017 | 0.018 | 0.015 |
HeartEW | 0.025 | 0.036 | 0.055 | 0.062 | 0.064 | 0.069 | 0.025 |
KrvskpEW | 0.002 | 0.007 | 0.007 | 0.051 | 0.044 | 0.012 | 0.010 |
M-of-n | 0.018 | 0.051 | 0.022 | 0.032 | 0.036 | 0.019 | 0.136 |
SonarEW | 0.027 | 0.033 | 0.029 | 0.030 | 0.059 | 0.043 | 0.037 |
SpectEW | 0.016 | 0.022 | 0.027 | 0.029 | 0.028 | 0.024 | 0.029 |
Tic-tac-toe | 0.007 | 0.012 | 0.020 | 0.021 | 0.025 | 0.017 | 0.014 |
Lymphography | 0.052 | 0.049 | 0.048 | 0.062 | 0.047 | 0.044 | 0.055 |
Dermatology | 0.007 | 0.014 | 0.008 | 0.014 | 0.075 | 0.050 | 0.008 |
Echocardiogram | 0.024 | 0.026 | 0.030 | 0.024 | 0.055 | 0.228 | 0.025 |
Hepatitis | 0.050 | 0.025 | 0.028 | 0.038 | 0.043 | 0.052 | 0.030 |
LungCancer | 0.092 | 0.151 | 0.180 | 0.233 | 0.194 | 0.093 | 0.183 |
Average | 0.026 | 0.036 | 0.033 | 0.080 | 0.048 | 0.046 | 0.040 |
Dataset | HBDESPO | BDA | EPSO | BGA | BBA | BGWO2 | HBEPSOD |
Zoo | 0.052 | 0.075 | 0.033 | 0.066 | 0.070 | 0.067 | 0.056 |
Wine EW | 0.019 | 0.030 | 0.018 | 0.026 | 0.080 | 0.057 | 0.017 |
IonosphereEW | 0.022 | 0.018 | 0.016 | 0.025 | 0.040 | 0.057 | 0.013 |
WaveformEW | 0.003 | 0.006 | 0.008 | 0.008 | 0.0123 | 0.007 | 0.006 |
BreastEW | 0.006 | 0.007 | 0.019 | 0.755 | 0.017 | 0.018 | 0.006 |
Breastcancer | 0.005 | 0.005 | 0.007 | 0.007 | 0.009 | 0.008 | 0.009 |
Congress | 0.007 | 0.016 | 0.008 | 0.013 | 0.019 | 0.015 | 0.008 |
Exactly | 0.071 | 0.119 | 0.082 | 0.078 | 0.020 | 0.016 | 0.117 |
Exactly2 | 0.009 | 0.015 | 0.009 | 0.019 | 0.017 | 0.018 | 0.015 |
HeartEW | 0.025 | 0.036 | 0.055 | 0.062 | 0.064 | 0.069 | 0.025 |
KrvskpEW | 0.002 | 0.007 | 0.007 | 0.051 | 0.044 | 0.012 | 0.010 |
M-of-n | 0.018 | 0.051 | 0.022 | 0.032 | 0.036 | 0.019 | 0.136 |
SonarEW | 0.027 | 0.033 | 0.029 | 0.030 | 0.059 | 0.043 | 0.037 |
SpectEW | 0.016 | 0.022 | 0.027 | 0.029 | 0.028 | 0.024 | 0.029 |
Tic-tac-toe | 0.007 | 0.012 | 0.020 | 0.021 | 0.025 | 0.017 | 0.014 |
Lymphography | 0.052 | 0.049 | 0.048 | 0.062 | 0.047 | 0.044 | 0.055 |
Dermatology | 0.007 | 0.014 | 0.008 | 0.014 | 0.075 | 0.050 | 0.008 |
Echocardiogram | 0.024 | 0.026 | 0.030 | 0.024 | 0.055 | 0.228 | 0.025 |
Hepatitis | 0.050 | 0.025 | 0.028 | 0.038 | 0.043 | 0.052 | 0.030 |
LungCancer | 0.092 | 0.151 | 0.180 | 0.233 | 0.194 | 0.093 | 0.183 |
Average | 0.026 | 0.036 | 0.033 | 0.080 | 0.048 | 0.046 | 0.040 |
Dataset | HBDESPO | BDA | EPSO | BGA | BBA | BGWO2 | HBEPSOD |
Zoo | 0.844 | 0.788 | 0.791 | 0.863 | 0.799 | 0.851 | 0.852 |
Wine EW | 0.916 | 0.923 | 0.881 | 0.886 | 0.726 | 0.896 | 0.888 |
IonosphereEW | 0.835 | 0.799 | 0.829 | 0.828 | 0.817 | 0.824 | 0.810 |
WaveformEW | 0.823 | 0.807 | 0.809 | 0.806 | 0.779 | 0.819 | 0.806 |
BreastEW | 0.949 | 0.944 | 0.931 | 0.892 | 0.842 | 0.908 | 0.926 |
Breastcancer | 0.960 | 0.956 | 0.956 | 0.957 | 0.957 | 0.957 | 0.958 |
Congress | 0.945 | 0.931 | 0.943 | 0.915 | 0.893 | 0.928 | 0.935 |
Exactly | 0.895 | 0.798 | 0.884 | 0.687 | 0.647 | 0.680 | 0.846 |
Exactly2 | 0.746 | 0.739 | 0.738 | 0.734 | 0.711 | 0.732 | 0.736 |
HeartEW | 0.815 | 0.81 | 0.776 | 0.711 | 0.648 | 0.702 | 0.811 |
KrvskpEW | 0.959 | 0.954 | 0.958 | 0.906 | 0.772 | 0.917 | 0.958 |
M-of-n | 0.978 | 0.949 | 0.975 | 0.892 | 0.719 | 0.843 | 0.957 |
SonarEW | 0.705 | 0.658 | 0.682 | 0.694 | 0.678 | 0.682 | 0.682 |
SpectEW | 0.762 | 0.752 | 0.757 | 0.750 | 0.755 | 0.777 | 0.747 |
Tic-tac-toe | 0.748 | 0.745 | 0.740 | 0.734 | 0.647 | 0.713 | 0.737 |
Lymphography | 0.406 | 0.417 | 0.354 | 0.416 | 0.422 | 0.379 | 0.411 |
Dermatology | 0.958 | 0.940 | 0.952 | 0.95 | 0.802 | 0.908 | 0.945 |
Echocardiogram | 0.875 | 0.893 | 0.906 | 0.852 | 0.861 | 0.877 | 0.863 |
Hepatitis | 0.819 | 0.788 | 0.813 | 0.798 | 0.788 | 0.788 | 0.803 |
LungCancer | 0.481 | 0.427 | 0.390 | 0.409 | 0.343 | 0.345 | 0.345 |
Average | 0.821 | 0.801 | 0.803 | 0.784 | 0.730 | 0.776 | 0.801 |
Dataset | HBDESPO | BDA | EPSO | BGA | BBA | BGWO2 | HBEPSOD |
Zoo | 0.844 | 0.788 | 0.791 | 0.863 | 0.799 | 0.851 | 0.852 |
Wine EW | 0.916 | 0.923 | 0.881 | 0.886 | 0.726 | 0.896 | 0.888 |
IonosphereEW | 0.835 | 0.799 | 0.829 | 0.828 | 0.817 | 0.824 | 0.810 |
WaveformEW | 0.823 | 0.807 | 0.809 | 0.806 | 0.779 | 0.819 | 0.806 |
BreastEW | 0.949 | 0.944 | 0.931 | 0.892 | 0.842 | 0.908 | 0.926 |
Breastcancer | 0.960 | 0.956 | 0.956 | 0.957 | 0.957 | 0.957 | 0.958 |
Congress | 0.945 | 0.931 | 0.943 | 0.915 | 0.893 | 0.928 | 0.935 |
Exactly | 0.895 | 0.798 | 0.884 | 0.687 | 0.647 | 0.680 | 0.846 |
Exactly2 | 0.746 | 0.739 | 0.738 | 0.734 | 0.711 | 0.732 | 0.736 |
HeartEW | 0.815 | 0.81 | 0.776 | 0.711 | 0.648 | 0.702 | 0.811 |
KrvskpEW | 0.959 | 0.954 | 0.958 | 0.906 | 0.772 | 0.917 | 0.958 |
M-of-n | 0.978 | 0.949 | 0.975 | 0.892 | 0.719 | 0.843 | 0.957 |
SonarEW | 0.705 | 0.658 | 0.682 | 0.694 | 0.678 | 0.682 | 0.682 |
SpectEW | 0.762 | 0.752 | 0.757 | 0.750 | 0.755 | 0.777 | 0.747 |
Tic-tac-toe | 0.748 | 0.745 | 0.740 | 0.734 | 0.647 | 0.713 | 0.737 |
Lymphography | 0.406 | 0.417 | 0.354 | 0.416 | 0.422 | 0.379 | 0.411 |
Dermatology | 0.958 | 0.940 | 0.952 | 0.95 | 0.802 | 0.908 | 0.945 |
Echocardiogram | 0.875 | 0.893 | 0.906 | 0.852 | 0.861 | 0.877 | 0.863 |
Hepatitis | 0.819 | 0.788 | 0.813 | 0.798 | 0.788 | 0.788 | 0.803 |
LungCancer | 0.481 | 0.427 | 0.390 | 0.409 | 0.343 | 0.345 | 0.345 |
Average | 0.821 | 0.801 | 0.803 | 0.784 | 0.730 | 0.776 | 0.801 |
Dataset | HBDESPO | BDA | EPSO | BGA | BBA | BGWO2 | HBEPSOD |
Zoo | 0.293 | 0.331 | 0.356 | 0.412 | 0.512 | 0.473 | 0.4 |
Wine EW | 0.284 | 0.338 | 0.4 | 0.315 | 0.538 | 0.516 | 0.338 |
IonosphereEW | 0.367 | 0.397 | 0.388 | 0.402 | 0.526 | 0.541 | 0.397 |
WaveformEW | 0.633 | 0.666 | 0.709 | 0.676 | 0.634 | 1 | 0.752 |
BreastEW | 0.241 | 0.283 | 0.241 | 0.290 | 0.480 | 0.470 | 0.3 |
Breastcancer | 0.411 | 0.422 | 0.511 | 0.566 | 0.511 | 0.644 | 0.544 |
Congress | 0.306 | 0.337 | 0.325 | 0.412 | 0.493 | 0.575 | 0.318 |
Exactly | 0.469 | 0.507 | 0.507 | 0.561 | 0.538 | 0.576 | 0.523 |
Exactly2 | 0.392 | 0.392 | 0.492 | 0.4 | 0.546 | 0.8 | 0.415 |
HeartEW | 0.391 | 0.407 | 0.407 | 0.415 | 0.492 | 0.430 | 0.4 |
KrvskpEW | 0.486 | 0.475 | 0.502 | 0.530 | 0.513 | 0.633 | 0.516 |
M-of-n | 0.515 | 0.530 | 0.476 | 0.576 | 0.446 | 0.923 | 0.515 |
SonarEW | 0.44 | 0.413 | 0.463 | 0.42 | 0.521 | 0.533 | 0.475 |
SpectEW | 0.413 | 0.454 | 0.463 | 0.425 | 0.481 | 0.529 | 0.440 |
Tic-tac-toe | 0.555 | 0.555 | 0.666 | 0.511 | 0.577 | 0.866 | 0.533 |
Lymphography | 0.39 | 0.438 | 0.416 | 0.4 | 0.461 | 0.535 | 0.45 |
Dermatology | 0.5 | 0.411 | 0.511 | 0.479 | 0.494 | 0.544 | 0.5 |
Echocardiogram | 0.225 | 0.233 | 0.266 | 0.283 | 0.508 | 0.483 | 0.25 |
Hepatitis | 0.273 | 0.273 | 0.321 | 0.231 | 0.515 | 0.431 | 0.294 |
LungCancer | 0.35 | 0.353 | 0.423 | 0.380 | 0.498 | 0.526 | 0.357 |
Average | 0.397 | 0.411 | 0.442 | 0.434 | 0.514 | 0.601 | 0.436 |
Dataset | HBDESPO | BDA | EPSO | BGA | BBA | BGWO2 | HBEPSOD |
Zoo | 0.293 | 0.331 | 0.356 | 0.412 | 0.512 | 0.473 | 0.4 |
Wine EW | 0.284 | 0.338 | 0.4 | 0.315 | 0.538 | 0.516 | 0.338 |
IonosphereEW | 0.367 | 0.397 | 0.388 | 0.402 | 0.526 | 0.541 | 0.397 |
WaveformEW | 0.633 | 0.666 | 0.709 | 0.676 | 0.634 | 1 | 0.752 |
BreastEW | 0.241 | 0.283 | 0.241 | 0.290 | 0.480 | 0.470 | 0.3 |
Breastcancer | 0.411 | 0.422 | 0.511 | 0.566 | 0.511 | 0.644 | 0.544 |
Congress | 0.306 | 0.337 | 0.325 | 0.412 | 0.493 | 0.575 | 0.318 |
Exactly | 0.469 | 0.507 | 0.507 | 0.561 | 0.538 | 0.576 | 0.523 |
Exactly2 | 0.392 | 0.392 | 0.492 | 0.4 | 0.546 | 0.8 | 0.415 |
HeartEW | 0.391 | 0.407 | 0.407 | 0.415 | 0.492 | 0.430 | 0.4 |
KrvskpEW | 0.486 | 0.475 | 0.502 | 0.530 | 0.513 | 0.633 | 0.516 |
M-of-n | 0.515 | 0.530 | 0.476 | 0.576 | 0.446 | 0.923 | 0.515 |
SonarEW | 0.44 | 0.413 | 0.463 | 0.42 | 0.521 | 0.533 | 0.475 |
SpectEW | 0.413 | 0.454 | 0.463 | 0.425 | 0.481 | 0.529 | 0.440 |
Tic-tac-toe | 0.555 | 0.555 | 0.666 | 0.511 | 0.577 | 0.866 | 0.533 |
Lymphography | 0.39 | 0.438 | 0.416 | 0.4 | 0.461 | 0.535 | 0.45 |
Dermatology | 0.5 | 0.411 | 0.511 | 0.479 | 0.494 | 0.544 | 0.5 |
Echocardiogram | 0.225 | 0.233 | 0.266 | 0.283 | 0.508 | 0.483 | 0.25 |
Hepatitis | 0.273 | 0.273 | 0.321 | 0.231 | 0.515 | 0.431 | 0.294 |
LungCancer | 0.35 | 0.353 | 0.423 | 0.380 | 0.498 | 0.526 | 0.357 |
Average | 0.397 | 0.411 | 0.442 | 0.434 | 0.514 | 0.601 | 0.436 |
Dataset | HBDESPO | BDA | EPSO | BGA | BBA | BGWO2 | HBEPSOD |
Zoo | 161 | 105 | 143 | 156 | 112 | 130 | 140 |
Wine EW | 24.13 | 374.67 | 648.48 | 540.52 | 11784.7 | 20939.7 | 41.169 |
IonosphereEW | 3.602 | 3.986 | 3.870 | 4.769 | 4.154 | 5.042 | 4.056 |
WaveformEW | 2.355 | 2.314 | 2.314 | 2.165 | 2.029 | 3.456 | 2.454 |
BreastEW | 7.2E+13 | 2.5E+11 | 3.3E+13 | 1.4E+13 | 5.7E+12 | 6.9E+13 | 3.1E+11 |
Breastcancer | 1.190 | 0.748 | 1.070 | 0.923 | 0.942 | 1.105 | 0.884 |
Congress | 48.584 | 31.797 | 13.996 | 13.045 | 11.003 | 18.317 | 22.088 |
Exactly | 0.391 | 0.259 | 0.131 | 0.378 | 0.350 | 0.282 | 0.144 |
Exactly2 | 0.395 | 0.240 | 0.267 | 0.287 | 0.200 | 0.237 | 0.227 |
HeartEW | 3.788 | 3.424 | 3.357 | 140.64 | 161.62 | 430.07 | 2.197 |
KrvskpEW | 1396.5 | 544.21 | 940.24 | 1023.2 | 639.91 | 1187.5 | 913.89 |
M-of-n | 1.791 | 1.711 | 1.735 | 1.786 | 1.652 | 1.373 | 1.693 |
SonarEW | 6.4E+6 | 7.3E+6 | 8.2E+6 | 5.5E+6 | 8.2E+6 | 1.2E+7 | 9.5E+6 |
SpectEW | 0.008 | 0.006 | 0.005 | 0.004 | 0.006 | 0.006 | 0.006 |
Tic-tac-toe | 0.168 | 0.090 | 0.161 | 0.119 | 0.136 | 0.117 | 0.134 |
Lymphography | 9.77 | 3.13 | 9.18 | 2.43 | 4.41 | 2.73 | 3.51 |
Dermatology | 400 | 269 | 343 | 148 | 210 | 174 | 207 |
Echocardiogram | 158.28 | 579.06 | 1376 | 62931 | 130939 | 53037 | 985.60 |
Hepatitis | 5.963 | 3.491 | 51.80 | 14.420 | 132.03 | 53037 | 84.211 |
LungCancer | 42.973 | 31.148 | 40.203 | 29.405 | 30.810 | 33.615 | 22.220 |
Average | 3.6E+12 | 1.3E+10 | 1.6E+12 | 6.9E+11 | 2.8E+11 | 3.4E+11 | 1.5E+10 |
Dataset | HBDESPO | BDA | EPSO | BGA | BBA | BGWO2 | HBEPSOD |
Zoo | 161 | 105 | 143 | 156 | 112 | 130 | 140 |
Wine EW | 24.13 | 374.67 | 648.48 | 540.52 | 11784.7 | 20939.7 | 41.169 |
IonosphereEW | 3.602 | 3.986 | 3.870 | 4.769 | 4.154 | 5.042 | 4.056 |
WaveformEW | 2.355 | 2.314 | 2.314 | 2.165 | 2.029 | 3.456 | 2.454 |
BreastEW | 7.2E+13 | 2.5E+11 | 3.3E+13 | 1.4E+13 | 5.7E+12 | 6.9E+13 | 3.1E+11 |
Breastcancer | 1.190 | 0.748 | 1.070 | 0.923 | 0.942 | 1.105 | 0.884 |
Congress | 48.584 | 31.797 | 13.996 | 13.045 | 11.003 | 18.317 | 22.088 |
Exactly | 0.391 | 0.259 | 0.131 | 0.378 | 0.350 | 0.282 | 0.144 |
Exactly2 | 0.395 | 0.240 | 0.267 | 0.287 | 0.200 | 0.237 | 0.227 |
HeartEW | 3.788 | 3.424 | 3.357 | 140.64 | 161.62 | 430.07 | 2.197 |
KrvskpEW | 1396.5 | 544.21 | 940.24 | 1023.2 | 639.91 | 1187.5 | 913.89 |
M-of-n | 1.791 | 1.711 | 1.735 | 1.786 | 1.652 | 1.373 | 1.693 |
SonarEW | 6.4E+6 | 7.3E+6 | 8.2E+6 | 5.5E+6 | 8.2E+6 | 1.2E+7 | 9.5E+6 |
SpectEW | 0.008 | 0.006 | 0.005 | 0.004 | 0.006 | 0.006 | 0.006 |
Tic-tac-toe | 0.168 | 0.090 | 0.161 | 0.119 | 0.136 | 0.117 | 0.134 |
Lymphography | 9.77 | 3.13 | 9.18 | 2.43 | 4.41 | 2.73 | 3.51 |
Dermatology | 400 | 269 | 343 | 148 | 210 | 174 | 207 |
Echocardiogram | 158.28 | 579.06 | 1376 | 62931 | 130939 | 53037 | 985.60 |
Hepatitis | 5.963 | 3.491 | 51.80 | 14.420 | 132.03 | 53037 | 84.211 |
LungCancer | 42.973 | 31.148 | 40.203 | 29.405 | 30.810 | 33.615 | 22.220 |
Average | 3.6E+12 | 1.3E+10 | 1.6E+12 | 6.9E+11 | 2.8E+11 | 3.4E+11 | 1.5E+10 |
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