
- Previous Article
- MFC Home
- This Issue
-
Next Article
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.
|
[2] |
H. Banati and M. Bajaj, Fire fly based feature selection approach,
IJCSI International Journal of Computer Science Issues, 8 (2011). |
[3] |
D. Bell and H. Wang,
A formalism for relevance and its application in feature subset selection, Mach. Learn., 41 (2000), 175-195.
|
[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. |
[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.
|
[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.
|
[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. |
[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.
|
[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. |
[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.
|
[13] |
A. Frank and A. Asuncion,
UCI Machine Learning Repository, 2010. |
[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. |
[16] |
S. Khalid, A survey of feature selection and feature extraction techniques in machine learning, Science and Information Conference (SAI), 2014. |
[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. |
[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.
|
[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.
|
[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.
|
[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. |
[22] |
E. G. Talbi,
A taxonomy of hybrid metaheuristics, Journal of Heuristics, 8 (2002), 541-565.
|
[23] |
D. Wolpert and W. Macready,
No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, 1 (1997), 67-72.
|
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.
|
[2] |
H. Banati and M. Bajaj, Fire fly based feature selection approach,
IJCSI International Journal of Computer Science Issues, 8 (2011). |
[3] |
D. Bell and H. Wang,
A formalism for relevance and its application in feature subset selection, Mach. Learn., 41 (2000), 175-195.
|
[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. |
[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.
|
[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.
|
[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. |
[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.
|
[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. |
[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.
|
[13] |
A. Frank and A. Asuncion,
UCI Machine Learning Repository, 2010. |
[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. |
[16] |
S. Khalid, A survey of feature selection and feature extraction techniques in machine learning, Science and Information Conference (SAI), 2014. |
[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. |
[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.
|
[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.
|
[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.
|
[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. |
[22] |
E. G. Talbi,
A taxonomy of hybrid metaheuristics, Journal of Heuristics, 8 (2002), 541-565.
|
[23] |
D. Wolpert and W. Macready,
No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, 1 (1997), 67-72.
|


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 |
[1] |
T. W. Leung, Chi Kin Chan, Marvin D. Troutt. A mixed simulated annealing-genetic algorithm approach to the multi-buyer multi-item joint replenishment problem: advantages of meta-heuristics. Journal of Industrial and Management Optimization, 2008, 4 (1) : 53-66. doi: 10.3934/jimo.2008.4.53 |
[2] |
Miao Yu. A solution of TSP based on the ant colony algorithm improved by particle swarm optimization. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 979-987. doi: 10.3934/dcdss.2019066 |
[3] |
Tao Zhang, Yue-Jie Zhang, Qipeng P. Zheng, P. M. Pardalos. A hybrid particle swarm optimization and tabu search algorithm for order planning problems of steel factories based on the Make-To-Stock and Make-To-Order management architecture. Journal of Industrial and Management Optimization, 2011, 7 (1) : 31-51. doi: 10.3934/jimo.2011.7.31 |
[4] |
Mohammed Abdulrazaq Kahya, Suhaib Abduljabbar Altamir, Zakariya Yahya Algamal. Improving whale optimization algorithm for feature selection with a time-varying transfer function. Numerical Algebra, Control and Optimization, 2021, 11 (1) : 87-98. doi: 10.3934/naco.2020017 |
[5] |
Min Zhang, Gang Li. Multi-objective optimization algorithm based on improved particle swarm in cloud computing environment. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1413-1426. doi: 10.3934/dcdss.2019097 |
[6] |
Junyuan Lin, Timothy A. Lucas. A particle swarm optimization model of emergency airplane evacuations with emotion. Networks and Heterogeneous Media, 2015, 10 (3) : 631-646. doi: 10.3934/nhm.2015.10.631 |
[7] |
Qifeng Cheng, Xue Han, Tingting Zhao, V S Sarma Yadavalli. Improved particle swarm optimization and neighborhood field optimization by introducing the re-sampling step of particle filter. Journal of Industrial and Management Optimization, 2019, 15 (1) : 177-198. doi: 10.3934/jimo.2018038 |
[8] |
Ning Lu, Ying Liu. Application of support vector machine model in wind power prediction based on particle swarm optimization. Discrete and Continuous Dynamical Systems - S, 2015, 8 (6) : 1267-1276. doi: 10.3934/dcdss.2015.8.1267 |
[9] |
Xia Zhao, Jianping Dou. Bi-objective integrated supply chain design with transportation choices: A multi-objective particle swarm optimization. Journal of Industrial and Management Optimization, 2019, 15 (3) : 1263-1288. doi: 10.3934/jimo.2018095 |
[10] |
Abdulrazzaq T. Abed, Azzam S. Y. Aladool. Applying particle swarm optimization based on Padé approximant to solve ordinary differential equation. Numerical Algebra, Control and Optimization, 2022, 12 (2) : 321-337. doi: 10.3934/naco.2021008 |
[11] |
Junjie Peng, Ning Chen, Jiayang Dai, Weihua Gui. A goethite process modeling method by Asynchronous Fuzzy Cognitive Network based on an improved constrained chicken swarm optimization algorithm. Journal of Industrial and Management Optimization, 2021, 17 (3) : 1269-1287. doi: 10.3934/jimo.2020021 |
[12] |
Omar Saber Qasim, Ahmed Entesar, Waleed Al-Hayani. Solving nonlinear differential equations using hybrid method between Lyapunov's artificial small parameter and continuous particle swarm optimization. Numerical Algebra, Control and Optimization, 2021, 11 (4) : 633-644. doi: 10.3934/naco.2021001 |
[13] |
Jianguo Dai, Wenxue Huang, Yuanyi Pan. A category-based probabilistic approach to feature selection. Big Data & Information Analytics, 2018 doi: 10.3934/bdia.2017020 |
[14] |
Yunmei Lu, Mingyuan Yan, Meng Han, Qingliang Yang, Yanqing Zhang. Privacy preserving feature selection and Multiclass Classification for horizontally distributed data. Mathematical Foundations of Computing, 2018, 1 (4) : 331-348. doi: 10.3934/mfc.2018016 |
[15] |
Nguyen Van Thoai. Decomposition branch and bound algorithm for optimization problems over efficient sets. Journal of Industrial and Management Optimization, 2008, 4 (4) : 647-660. doi: 10.3934/jimo.2008.4.647 |
[16] |
Tran Ngoc Thang, Nguyen Thi Bach Kim. Outcome space algorithm for generalized multiplicative problems and optimization over the efficient set. Journal of Industrial and Management Optimization, 2016, 12 (4) : 1417-1433. doi: 10.3934/jimo.2016.12.1417 |
[17] |
Feng Bao, Thomas Maier. Stochastic gradient descent algorithm for stochastic optimization in solving analytic continuation problems. Foundations of Data Science, 2020, 2 (1) : 1-17. doi: 10.3934/fods.2020001 |
[18] |
Paul B. Hermanns, Nguyen Van Thoai. Global optimization algorithm for solving bilevel programming problems with quadratic lower levels. Journal of Industrial and Management Optimization, 2010, 6 (1) : 177-196. doi: 10.3934/jimo.2010.6.177 |
[19] |
Mohamed A. Tawhid, Ahmed F. Ali. An effective hybrid firefly algorithm with the cuckoo search for engineering optimization problems. Mathematical Foundations of Computing, 2018, 1 (4) : 349-368. doi: 10.3934/mfc.2018017 |
[20] |
Zhongqiang Wu, Zongkui Xie. A multi-objective lion swarm optimization based on multi-agent. Journal of Industrial and Management Optimization, 2022 doi: 10.3934/jimo.2022001 |
Impact Factor:
Tools
Metrics
Other articles
by authors
[Back to Top]