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A kernel-free fuzzy support vector machine with Universum
1. | School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai 201620, China |
2. | School of Management, Shanghai University of International Business and Economics, Shanghai 201620, China |
Support vector machines with Universum are attractive for dealing with classification problems by incorporating prior information. In this paper, a quadratic function based kernel-free support vector machine with Universum is proposed for binary classification. To deal with noise and outliers, two fuzzy membership functions considering both information entropy and distance information are constructed for labeled and Universum data, respectively. The fuzzy membership function for Universum is also adopted for further selecting Universum data to improve the robustness. The proposed model corresponds to an efficiently solved convex quadratic programming. In the meanwhile, by avoiding the issue of choosing kernel functions, the proposed model saves more computational time when compared with other Universum-based support vector machines. Finally, some numerical tests are implemented on several data sets to validate the classification effectiveness of the proposed method. The numerical results illustrate the competitive performance when compared with some state-of-the-art support vector machines. Applications on two credit rating data sets are also conducted to distinguish the classification performance of the proposed method.
References:
[1] |
X. Bai and V. Cherkassky, Gender classification of human faces using inference through contradictions, In Proceedings of the IEEE International Joint Conference on Neural Networks, (2008), 746–750. |
[2] |
R. Batuwita and V. Palade,
FSVM-CIL: Fuzzy support vector machines for class imbalance learning, IEEE Transactions on Fuzzy Systems, 18 (2010), 558-571.
doi: 10.1109/TFUZZ.2010.2042721. |
[3] |
C. L. Blake and C. J. Merz, UCIrepository for machine learning databases [online], http//www.ics.uci.edu/ mlearn/MLRepository.html, 1998. |
[4] |
S. Chen and C. Zhang, Selecting informative Universum sample for semi-supervised learning, In Proceedings of the 21st International Joint Conference on Artificial Intelligence, (2009), 1016–1021. |
[5] |
V. Cherkassky, S. Dhar and W. Dai,
Practical conditions for effectiveness of the universum learning, IEEE Transactions on Neural Networks, 22 (2011), 1241-1255.
doi: 10.1109/TNN.2011.2157522. |
[6] |
P. Cho, M. Lee and W. Chang,
Instance-based entropy fuzzy support vector machine for imbalanced data, PAA Pattern Anal. Appl., 23 (2020), 1183-1202.
doi: 10.1007/s10044-019-00851-x. |
[7] |
C. Cortes and V. Vapnik,
Support-vector networks, Machine Learning, 20 (1995), 273-297.
doi: 10.1007/BF00994018. |
[8] |
J. Demšar,
Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res., 7 (2006), 1-30.
|
[9] |
Q. Fan, Z. Wang, D. Li, D. Gao and H. Zha,
Entropy-based fuzzy support vector machine for imbalanced datasets, Knowledge-Based Systems, 115 (2017), 87-99.
doi: 10.1016/j.knosys.2016.09.032. |
[10] |
D. Gupta, B. Richhariya and P. Borah,
A fuzzy twin support vector machine based on information entropy for class imbalance learning, Neural Computing and Applications, 31 (2019), 7153-7164.
doi: 10.1007/s00521-018-3551-9. |
[11] |
J. Huang and C. X. Ling,
Using AUC and accuracy in evaluating learning algorithms, IEEE Transactions on Knowledge and Data Engineering, 17 (2005), 299-310.
|
[12] |
L.-L. Li, X. Zhao, M.-L. Tseng and R. R. Tan,
Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm, Journal of Cleaner Production, 242 (2020), 118447.
doi: 10.1016/j.jclepro.2019.118447. |
[13] |
C.-F. Lin and S.-D. Wang,
Fuzzy support vector machines, IEEE Transactions on Neural Networks, 13 (2002), 464-471.
|
[14] |
W. Long, Y. Tang and Y. Tian,
Investor sentiment identification based on the universum SVM, Neural Computing and Applications, 30 (2018), 661-670.
doi: 10.1007/s00521-016-2684-y. |
[15] |
J. Luo, S.-C. Fang, Y. Bai and Z. Deng,
Fuzzy quadratic surface support vector machine based on Fisher discriminant analysis, J. Ind. Manag. Optim., 12 (2016), 357-373.
doi: 10.3934/jimo.2016.12.357. |
[16] |
J. Luo, S.-C. Fang, Z. Deng and X. Guo, Soft quadratic surface support vector machine for binary classification, Asia-Pac. J. Oper. Res., 33 (2016), 22pp.
doi: 10.1142/S0217595916500469. |
[17] |
J. Luo, X. Yan and Y. Tian,
Unsupervised quadratic surface support vector machine with application to credit risk assessment, European J. Oper. Res., 280 (2020), 1008-1017.
doi: 10.1016/j.ejor.2019.08.010. |
[18] |
J. Luo, X. Yang, Y. Tian and W. Yu,
Corporate and personal credit scoring via fuzzy non-kernel SVM with fuzzy within-class scatter, J. Ind. Manag. Optim., 16 (2020), 2743-2756.
doi: 10.3934/jimo.2019078. |
[19] |
A. Mousavi, Z. Gao, L. Han and A. Lim, Quadratic surface support vector machine with l1 norm regularization, J. Industrial and Management Optimization, 2021.
doi: 10.3934/jimo.2021046. |
[20] |
Z. Qi, Y. Tian and Y. Shi,
Twin support vector machine with universum data, Neural Networks, 36 (2012), 112-119.
doi: 10.1016/j.neunet.2012.09.004. |
[21] |
Z. Qi, Y. Tian and Y. Shi,
A nonparallel support vector machine for a classification problem with universum learning, J. Comput. Appl. Math., 263 (2014), 288-298.
doi: 10.1016/j.cam.2013.11.003. |
[22] |
S. Raghavendra and P. C. Deka,
Support vector machine applications in the field of hydrology: A review, Applied Soft Computing, 19 (2014), 372-386.
doi: 10.1016/j.asoc.2014.02.002. |
[23] |
B. Richhariya and M. Tanveer,
A fuzzy universum support vector machine based on information entropy, Machine Intelligence and Signal Analysis, 748 (2019), 569-582.
doi: 10.1007/978-981-13-0923-6_49. |
[24] |
B. Richhariya and M. Tanveer, A reduced universum twin support vector machine for class imbalance learning, Pattern Recognition, 102 (2020).
doi: 10.1016/j.patcog.2019.107150. |
[25] |
B. Richhariya, M. Tanveer, A. Rashid and A. D. N. Initiative et al.,
Diagnosis of Alzheimer's disease using universum support vector machine based recursive feature elimination (USVM-RFE), Biomedical Signal Processing and Control, 59 (2020), 101903.
doi: 10.1016/j.bspc.2020.101903. |
[26] |
Y. Tian, M. Sun, Z. Deng, J. Luo and Y. Li,
A new fuzzy set and nonkernel svm approach for mislabeled binary classification with applications, IEEE Transactions on Fuzzy Systems, 25 (2017), 1536-1545.
doi: 10.1109/TFUZZ.2017.2752138. |
[27] |
J. Weston, R. Collobert, F. Sinz, L. Bottou and V. Vapnik, Inference with the universum, In Proceedings of the 23rd International Conference on Machine Learning, (2006), 1009–1016.
doi: 10.1145/1143844.1143971. |
[28] |
Y. Xu, M. Chen, Z. Yang and G. Li,
$\nu$-twin support vector machine with Universum data for classification, Applied Intelligence, 44 (2016), 956-968.
|
show all references
References:
[1] |
X. Bai and V. Cherkassky, Gender classification of human faces using inference through contradictions, In Proceedings of the IEEE International Joint Conference on Neural Networks, (2008), 746–750. |
[2] |
R. Batuwita and V. Palade,
FSVM-CIL: Fuzzy support vector machines for class imbalance learning, IEEE Transactions on Fuzzy Systems, 18 (2010), 558-571.
doi: 10.1109/TFUZZ.2010.2042721. |
[3] |
C. L. Blake and C. J. Merz, UCIrepository for machine learning databases [online], http//www.ics.uci.edu/ mlearn/MLRepository.html, 1998. |
[4] |
S. Chen and C. Zhang, Selecting informative Universum sample for semi-supervised learning, In Proceedings of the 21st International Joint Conference on Artificial Intelligence, (2009), 1016–1021. |
[5] |
V. Cherkassky, S. Dhar and W. Dai,
Practical conditions for effectiveness of the universum learning, IEEE Transactions on Neural Networks, 22 (2011), 1241-1255.
doi: 10.1109/TNN.2011.2157522. |
[6] |
P. Cho, M. Lee and W. Chang,
Instance-based entropy fuzzy support vector machine for imbalanced data, PAA Pattern Anal. Appl., 23 (2020), 1183-1202.
doi: 10.1007/s10044-019-00851-x. |
[7] |
C. Cortes and V. Vapnik,
Support-vector networks, Machine Learning, 20 (1995), 273-297.
doi: 10.1007/BF00994018. |
[8] |
J. Demšar,
Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res., 7 (2006), 1-30.
|
[9] |
Q. Fan, Z. Wang, D. Li, D. Gao and H. Zha,
Entropy-based fuzzy support vector machine for imbalanced datasets, Knowledge-Based Systems, 115 (2017), 87-99.
doi: 10.1016/j.knosys.2016.09.032. |
[10] |
D. Gupta, B. Richhariya and P. Borah,
A fuzzy twin support vector machine based on information entropy for class imbalance learning, Neural Computing and Applications, 31 (2019), 7153-7164.
doi: 10.1007/s00521-018-3551-9. |
[11] |
J. Huang and C. X. Ling,
Using AUC and accuracy in evaluating learning algorithms, IEEE Transactions on Knowledge and Data Engineering, 17 (2005), 299-310.
|
[12] |
L.-L. Li, X. Zhao, M.-L. Tseng and R. R. Tan,
Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm, Journal of Cleaner Production, 242 (2020), 118447.
doi: 10.1016/j.jclepro.2019.118447. |
[13] |
C.-F. Lin and S.-D. Wang,
Fuzzy support vector machines, IEEE Transactions on Neural Networks, 13 (2002), 464-471.
|
[14] |
W. Long, Y. Tang and Y. Tian,
Investor sentiment identification based on the universum SVM, Neural Computing and Applications, 30 (2018), 661-670.
doi: 10.1007/s00521-016-2684-y. |
[15] |
J. Luo, S.-C. Fang, Y. Bai and Z. Deng,
Fuzzy quadratic surface support vector machine based on Fisher discriminant analysis, J. Ind. Manag. Optim., 12 (2016), 357-373.
doi: 10.3934/jimo.2016.12.357. |
[16] |
J. Luo, S.-C. Fang, Z. Deng and X. Guo, Soft quadratic surface support vector machine for binary classification, Asia-Pac. J. Oper. Res., 33 (2016), 22pp.
doi: 10.1142/S0217595916500469. |
[17] |
J. Luo, X. Yan and Y. Tian,
Unsupervised quadratic surface support vector machine with application to credit risk assessment, European J. Oper. Res., 280 (2020), 1008-1017.
doi: 10.1016/j.ejor.2019.08.010. |
[18] |
J. Luo, X. Yang, Y. Tian and W. Yu,
Corporate and personal credit scoring via fuzzy non-kernel SVM with fuzzy within-class scatter, J. Ind. Manag. Optim., 16 (2020), 2743-2756.
doi: 10.3934/jimo.2019078. |
[19] |
A. Mousavi, Z. Gao, L. Han and A. Lim, Quadratic surface support vector machine with l1 norm regularization, J. Industrial and Management Optimization, 2021.
doi: 10.3934/jimo.2021046. |
[20] |
Z. Qi, Y. Tian and Y. Shi,
Twin support vector machine with universum data, Neural Networks, 36 (2012), 112-119.
doi: 10.1016/j.neunet.2012.09.004. |
[21] |
Z. Qi, Y. Tian and Y. Shi,
A nonparallel support vector machine for a classification problem with universum learning, J. Comput. Appl. Math., 263 (2014), 288-298.
doi: 10.1016/j.cam.2013.11.003. |
[22] |
S. Raghavendra and P. C. Deka,
Support vector machine applications in the field of hydrology: A review, Applied Soft Computing, 19 (2014), 372-386.
doi: 10.1016/j.asoc.2014.02.002. |
[23] |
B. Richhariya and M. Tanveer,
A fuzzy universum support vector machine based on information entropy, Machine Intelligence and Signal Analysis, 748 (2019), 569-582.
doi: 10.1007/978-981-13-0923-6_49. |
[24] |
B. Richhariya and M. Tanveer, A reduced universum twin support vector machine for class imbalance learning, Pattern Recognition, 102 (2020).
doi: 10.1016/j.patcog.2019.107150. |
[25] |
B. Richhariya, M. Tanveer, A. Rashid and A. D. N. Initiative et al.,
Diagnosis of Alzheimer's disease using universum support vector machine based recursive feature elimination (USVM-RFE), Biomedical Signal Processing and Control, 59 (2020), 101903.
doi: 10.1016/j.bspc.2020.101903. |
[26] |
Y. Tian, M. Sun, Z. Deng, J. Luo and Y. Li,
A new fuzzy set and nonkernel svm approach for mislabeled binary classification with applications, IEEE Transactions on Fuzzy Systems, 25 (2017), 1536-1545.
doi: 10.1109/TFUZZ.2017.2752138. |
[27] |
J. Weston, R. Collobert, F. Sinz, L. Bottou and V. Vapnik, Inference with the universum, In Proceedings of the 23rd International Conference on Machine Learning, (2006), 1009–1016.
doi: 10.1145/1143844.1143971. |
[28] |
Y. Xu, M. Chen, Z. Yang and G. Li,
$\nu$-twin support vector machine with Universum data for classification, Applied Intelligence, 44 (2016), 956-968.
|

KFFUSVM | QSSVM | EFSVM | USVM | FUSVM | |
Data1a | |||||
Data1b | |||||
Data2a | |||||
Data2b |
KFFUSVM | QSSVM | EFSVM | USVM | FUSVM | |
Data1a | |||||
Data1b | |||||
Data2a | |||||
Data2b |
KFFUSVM | QSSVM | EFSVM | USVM | FUSVM | |
Time | 1.83 | 0.12 | 4.85 | 4.24 | 4.90 |
KFFUSVM | QSSVM | EFSVM | USVM | FUSVM | |
Time | 1.83 | 0.12 | 4.85 | 4.24 | 4.90 |
Dataset | Number | Dimension | Positive number | Negative number |
Ecoli0146Vs5 | 280 | 6 | 260 | 20 |
Ecoli034Vs5 | 200 | 7 | 180 | 20 |
Glass016Vs5 | 184 | 9 | 175 | 9 |
Glass0 | 214 | 9 | 144 | 70 |
Ecoli01Vs5 | 240 | 7 | 220 | 20 |
Absenteeism | 740 | 20 | 420 | 320 |
Ecoli067Vs5 | 220 | 7 | 200 | 20 |
CMC | 1473 | 9 | 844 | 629 |
Glass4 | 214 | 9 | 201 | 13 |
BupaLiver | 345 | 6 | 200 | 145 |
Transfusion | 748 | 4 | 570 | 178 |
Ecoli0147Vs56 | 332 | 6 | 307 | 25 |
Yeast2Vs8 | 483 | 8 | 463 | 20 |
Ecoli4 | 336 | 7 | 316 | 20 |
Vehicle0 | 846 | 18 | 647 | 199 |
Haberman | 306 | 3 | 225 | 81 |
Dataset | Number | Dimension | Positive number | Negative number |
Ecoli0146Vs5 | 280 | 6 | 260 | 20 |
Ecoli034Vs5 | 200 | 7 | 180 | 20 |
Glass016Vs5 | 184 | 9 | 175 | 9 |
Glass0 | 214 | 9 | 144 | 70 |
Ecoli01Vs5 | 240 | 7 | 220 | 20 |
Absenteeism | 740 | 20 | 420 | 320 |
Ecoli067Vs5 | 220 | 7 | 200 | 20 |
CMC | 1473 | 9 | 844 | 629 |
Glass4 | 214 | 9 | 201 | 13 |
BupaLiver | 345 | 6 | 200 | 145 |
Transfusion | 748 | 4 | 570 | 178 |
Ecoli0147Vs56 | 332 | 6 | 307 | 25 |
Yeast2Vs8 | 483 | 8 | 463 | 20 |
Ecoli4 | 336 | 7 | 316 | 20 |
Vehicle0 | 846 | 18 | 647 | 199 |
Haberman | 306 | 3 | 225 | 81 |
Dataset | KFFUSVM | QSSVM | EFSVM | USVM | FUSVM |
Ecoli0146Vs5 | |||||
Ecoli034Vs5 | |||||
Glass016Vs5 | |||||
Glass0 | |||||
Ecoli01Vs5 | |||||
Absenteeism | |||||
Ecoli067Vs5 | |||||
CMC | |||||
Glass4 | |||||
BupaLiver | |||||
Transfusion | |||||
Ecoli0147Vs56 | |||||
Yeast2Vs8 | |||||
Ecoli4 | |||||
Vehicle0 | |||||
Haberman |
Dataset | KFFUSVM | QSSVM | EFSVM | USVM | FUSVM |
Ecoli0146Vs5 | |||||
Ecoli034Vs5 | |||||
Glass016Vs5 | |||||
Glass0 | |||||
Ecoli01Vs5 | |||||
Absenteeism | |||||
Ecoli067Vs5 | |||||
CMC | |||||
Glass4 | |||||
BupaLiver | |||||
Transfusion | |||||
Ecoli0147Vs56 | |||||
Yeast2Vs8 | |||||
Ecoli4 | |||||
Vehicle0 | |||||
Haberman |
Dataset | KFFUSVM | QSSVM | EFSVM | USVM | FUSVM |
Ecoli0146Vs5 | 1 | 4 | 5 | 3 | 2 |
Ecoli034Vs5 | 1 | 5 | 4 | 2 | 3 |
Glass016Vs5 | 4.5 | 3 | 4.5 | 1.5 | 1.5 |
Glass0 | 5 | 4 | 3 | 1 | 2 |
Ecoli01Vs5 | 1 | 5 | 4 | 3 | 2 |
Absenteeism | 1.5 | 1.5 | 3 | 4.5 | 4.5 |
Ecoli067Vs5 | 2 | 4 | 5 | 3 | 1 |
CMC | 2 | 1 | 4 | 3 | 5 |
Glass4 | 3 | 5 | 4 | 2 | 1 |
BupaLiver | 2 | 1 | 5 | 4 | 3 |
Transfusion | 1 | 2 | 3 | 5 | 4 |
Ecoli0147Vs56 | 1 | 4 | 5 | 2 | 3 |
Yeast2Vs8 | 4.5 | 4.5 | 1 | 2.5 | 2.5 |
Ecoli4 | 1 | 4 | 5 | 3 | 2 |
Vehicle0 | 1 | 2 | 3 | 4.5 | 4.5 |
Haberman | 1 | 2 | 5 | 3 | 4 |
Average rank | 2.03 | 3.25 | 3.97 | 2.94 | 2.81 |
Dataset | KFFUSVM | QSSVM | EFSVM | USVM | FUSVM |
Ecoli0146Vs5 | 1 | 4 | 5 | 3 | 2 |
Ecoli034Vs5 | 1 | 5 | 4 | 2 | 3 |
Glass016Vs5 | 4.5 | 3 | 4.5 | 1.5 | 1.5 |
Glass0 | 5 | 4 | 3 | 1 | 2 |
Ecoli01Vs5 | 1 | 5 | 4 | 3 | 2 |
Absenteeism | 1.5 | 1.5 | 3 | 4.5 | 4.5 |
Ecoli067Vs5 | 2 | 4 | 5 | 3 | 1 |
CMC | 2 | 1 | 4 | 3 | 5 |
Glass4 | 3 | 5 | 4 | 2 | 1 |
BupaLiver | 2 | 1 | 5 | 4 | 3 |
Transfusion | 1 | 2 | 3 | 5 | 4 |
Ecoli0147Vs56 | 1 | 4 | 5 | 2 | 3 |
Yeast2Vs8 | 4.5 | 4.5 | 1 | 2.5 | 2.5 |
Ecoli4 | 1 | 4 | 5 | 3 | 2 |
Vehicle0 | 1 | 2 | 3 | 4.5 | 4.5 |
Haberman | 1 | 2 | 5 | 3 | 4 |
Average rank | 2.03 | 3.25 | 3.97 | 2.94 | 2.81 |
Dataset | KFFUSVM | QSSVM | EFSVM | USVM | FUSVM |
Ecoli0146Vs5 | |||||
Ecoli034Vs5 | |||||
Glass016Vs5 | |||||
Glass0 | |||||
Ecoli01Vs5 | |||||
Absenteeism | |||||
Ecoli067Vs5 | |||||
CMC | |||||
Glass4 | |||||
BupaLiver | |||||
Transfusion | |||||
Ecoli0147Vs56 | |||||
Yeast2Vs8 | |||||
Ecoli4 | |||||
Vehicle0 | |||||
Haberman |
Dataset | KFFUSVM | QSSVM | EFSVM | USVM | FUSVM |
Ecoli0146Vs5 | |||||
Ecoli034Vs5 | |||||
Glass016Vs5 | |||||
Glass0 | |||||
Ecoli01Vs5 | |||||
Absenteeism | |||||
Ecoli067Vs5 | |||||
CMC | |||||
Glass4 | |||||
BupaLiver | |||||
Transfusion | |||||
Ecoli0147Vs56 | |||||
Yeast2Vs8 | |||||
Ecoli4 | |||||
Vehicle0 | |||||
Haberman |
Dataset | KFFUSVM | QSSVM | EFSVM | USVM | FUSVM |
Ecoli0146Vs5 | 2 | 5 | 4 | 3 | 1 |
Ecoli034Vs5 | 3 | 5 | 4 | 1 | 2 |
Glass016Vs5 | 3 | 4 | 5 | 1.5 | 1.5 |
Glass0 | 4 | 5 | 3 | 2 | 1 |
Ecoli01Vs5 | 1 | 5 | 4 | 3 | 2 |
Absenteeism | 1.5 | 1.5 | 3 | 4.5 | 4.5 |
Ecoli067Vs5 | 1 | 5 | 3 | 4 | 2 |
CMC | 1 | 2 | 5 | 3 | 4 |
Glass4 | 4 | 5 | 3 | 2 | 1 |
BupaLiver | 1.5 | 1.5 | 5 | 4 | 3 |
Transfusion | 2 | 5 | 3 | 4 | 1 |
Ecoli0147Vs56 | 1 | 4 | 5 | 3 | 2 |
Yeast2Vs8 | 4.5 | 4.5 | 1 | 2.5 | 2.5 |
Ecoli4 | 1 | 5 | 4 | 3 | 2 |
Vehicle0 | 1 | 2 | 3 | 4.5 | 4.5 |
Haberman | 1 | 3 | 4 | 2 | 5 |
Average rank | 2.03 | 3.91 | 3.69 | 2.94 | 2.44 |
Dataset | KFFUSVM | QSSVM | EFSVM | USVM | FUSVM |
Ecoli0146Vs5 | 2 | 5 | 4 | 3 | 1 |
Ecoli034Vs5 | 3 | 5 | 4 | 1 | 2 |
Glass016Vs5 | 3 | 4 | 5 | 1.5 | 1.5 |
Glass0 | 4 | 5 | 3 | 2 | 1 |
Ecoli01Vs5 | 1 | 5 | 4 | 3 | 2 |
Absenteeism | 1.5 | 1.5 | 3 | 4.5 | 4.5 |
Ecoli067Vs5 | 1 | 5 | 3 | 4 | 2 |
CMC | 1 | 2 | 5 | 3 | 4 |
Glass4 | 4 | 5 | 3 | 2 | 1 |
BupaLiver | 1.5 | 1.5 | 5 | 4 | 3 |
Transfusion | 2 | 5 | 3 | 4 | 1 |
Ecoli0147Vs56 | 1 | 4 | 5 | 3 | 2 |
Yeast2Vs8 | 4.5 | 4.5 | 1 | 2.5 | 2.5 |
Ecoli4 | 1 | 5 | 4 | 3 | 2 |
Vehicle0 | 1 | 2 | 3 | 4.5 | 4.5 |
Haberman | 1 | 3 | 4 | 2 | 5 |
Average rank | 2.03 | 3.91 | 3.69 | 2.94 | 2.44 |
Dataset | KFFUSVM | QSSVM | EFSVM | USVM | FUSVM |
Automobile (ACC) |
|||||
Automobile' (ACC) |
|||||
Automobile (AUC) |
|||||
Automobile' (AUC) |
|||||
Dataset | KFFUSVM | QSSVM | EFSVM | USVM | FUSVM |
Automobile (ACC) |
|||||
Automobile' (ACC) |
|||||
Automobile (AUC) |
|||||
Automobile' (AUC) |
|||||
Dataset | KFFUSVM | QSSVM | EFSVM | USVM | FUSVM |
Australian (ACC) |
|||||
Australian' (ACC) |
|||||
Australian (AUC) |
|||||
Australian' (AUC) |
|||||
Dataset | KFFUSVM | QSSVM | EFSVM | USVM | FUSVM |
Australian (ACC) |
|||||
Australian' (ACC) |
|||||
Australian (AUC) |
|||||
Australian' (AUC) |
|||||
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