Figure 1.
ROC curves for one test in case C1
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A new relaxed CQ algorithm for solving split feasibility problems in Hilbert spaces and its applications
April
2019, 15(2): 985-999.
doi: 10.3934/jimo.2018081
Predicting non-life insurer's insolvency using non-kernel fuzzy quadratic surface support vector machines
| 1. | School of Business Administration and Collaborative Innovation Center of Financial Security, Southwestern University of Finance and Economics, Chengdu 611130, China |
| 2. | School of Insurance and Collaborative Innovation Center of Financial Security, Southwestern University of Finance and Economics, Chengdu 611130, China |
| 3. | Department of Finance, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA |
| 4. | School of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, China |
Due to the serious consequence caused by insurers' insolvency, how to accurately predict insolvency becomes a very important issue in this area. Many methods have been developed to do this task by using some firm-level financial information. In this paper, we propose a new approach which incorporates several macroeconomic factors in the model and applies feature selection to eliminate the bad effect of some unrelated variables. In this way, we can obtain a more comprehensive and accurate model. More importantly, our method is based on the state-of-the-art non-kernel fuzzy quadratic surface support vector machine (FQSSVM) model which not only performs superiorly in prediction, but also becomes very applicable to the users. Finally, we conduct some numerical experiments based on the real data of non-lifer insurers from USA to show the predictive power and efficiency of our proposed method compared with other benchmark methods. Specifically, in a reasonable computational time, FQSSVM has the most accurate prediction rate and least Type Ⅰ and Type Ⅱ errors.
Keywords:
Non-life insurer,
predicting insolvency,
macroeconomic factors,
feature selection,
non-kernel FQSSVM.
Mathematics Subject Classification:
90B90, 90B50.
Citation:
Ye Tian, Wei Yang, Gene Lai, Menghan Zhao. Predicting non-life insurer's insolvency using non-kernel fuzzy quadratic surface support vector machines. Journal of Industrial and Management Optimization,
2019, 15
(2)
: 985-999.
doi: 10.3934/jimo.2018081
![]()
References:
| [1] |
M. Al-Smadi,
Credit Risk, Macroeconomic and Bank Specific Factors, VDM Verlag Dr. M$ü$ller, 2011. |
| [2] |
E. Baranoff, T. Sager and T. Shively,
A semiparametric stochastic spline model as a managerial tool for potential insolvency, Journal of Risk and Insurance, 67 (2000), 369-396.
doi: 10.2307/253834. |
| [3] |
A. Best,
Best's insolvency study-property/casualty insurers, Best's Review-Property/Casualty Insurance Edition, (1991), 16-23.
|
| [4] |
J. Carson and R. Hoyt,
Life insurer financial distress: Classification models and empirical evidence, Journal of Risk and Insurance, 62 (1995), 764-775.
doi: 10.2307/253595. |
| [5] |
J. Cheng and M. Weiss,
The fole of rbc, hurricane exposure, bond portfolio duration, and macroeconomic and industry-wide factors in property-liability insolvency prediction, Journal of Risk and Insurance, 79 (2012), 723-750.
|
| [6] |
H. Chew and C. Lim,
On regularisation parameter transformation of support vector machines, Journal of Industrial and Management Optimization, 5 (2009), 403-415.
doi: 10.3934/jimo.2009.5.403. |
| [7] |
S. Cho, J. Kim and J. Bae,
An integrative model with subject weight based on neural network learning for bankruptcy prediction, Expert Systems with Applications, 36 (2009), 403-410.
doi: 10.1016/j.eswa.2007.09.060. |
| [8] |
J. Cummins, M. Grace and R. Phillips,
Regulatory solvency prediction in property-liability insurance: Risk-based capital, audit ratios, and cash flow simulation, Journal of Risk and Insurance, 66 (1999), 417-458.
doi: 10.2307/253555. |
| [9] |
U. Dellepiane, M. Marcantonio, E. Laghi and S. Renzi,
Bankruptcy prediction using support vector machines and feature selection during the recent financial crisis, International Journal of Economics and Finance, 7 (2015), 182-194.
doi: 10.5539/ijef.v7n8p182. |
| [10] |
A. Dimitras, R. Slowinski, R. Susmaga and C. Zopounidis,
Business failure using rough set, European Journal of Operational Research, 114 (1998), 263-280.
|
| [11] |
P. Du Jardin,
Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy, Neurocomputing, 73 (2010), 2047-2060.
doi: 10.1016/j.neucom.2009.11.034. |
| [12] |
M. Grace, S. Harrington and R. Klein,
Risk-based captial and solvency screening in property-liability insurance: Hypothesis and empirical tests, Journal of Risk and Insurance, 65 (1998), 213-243.
|
| [13] |
M. Grant and S. Boyd,
Cvx: Matlab Software for Disciplined Programming, version 1.2, Technical report, http://cvxr.com/cvx 2010. |
| [14] |
S. Hsiao and T. Whang,
A study of financial insolvency prediction model for life insurers, Expert Systems with Applications, 36 (2009), 6100-6107.
doi: 10.1016/j.eswa.2008.07.024. |
| [15] |
M. Kim and D. Kang,
Ensemble with neural networks for bankruptcy prediction, Expert Systems with Applications, 37 (2010), 3373-3379.
doi: 10.1016/j.eswa.2009.10.012. |
| [16] |
G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui and M. Jordan,
Learning the kernel matrix with semi-definite programming, Journal of Machine Learning Research, 5 (2004), 27-72.
|
| [17] |
S. Lee and J. Urrutia,
Analysis and prediction of insolvency in the property-liability insurance industry: A comparison of logit and hazard models, Journal of Risk and Insurance, 63 (1996), 121-130.
doi: 10.2307/253520. |
| [18] |
J. Luo, S.-C. Fang, Y. Bai and Z. Deng,
Fuzzy quadratic surface support vector machine based on fisher discriminant analysis, Journal of Industrial and Management Optimization, 12 (2016), 357-373.
doi: 10.3934/jimo.2016.12.357. |
| [19] |
J. Maria, S. Sanch and B. Carlos,
Prediction of insolvency in non-life insurance companies using support vector machines, genetic algorithms and simulated annealing, Fuzzy Economic Review, 9 (2004), 79-94.
|
| [20] |
J. Min and Y. Lee,
Bankrupty prediction using support vector machine with optimal choice of kernel function parameters, Expert Systems with Applications, 28 (2005), 603-614.
|
| [21] |
S. Min, J. Lee and I. Han,
Hybrid genetic algorithms and support vector machines for bankruptcy prediction, Expert Systems with Applications, 31 (2006), 652-660.
doi: 10.1016/j.eswa.2005.09.070. |
| [22] |
S. Pottier and D. Sommer,
Empirical evidence on the value of group-level financial information in insurer solvency surveillance, Risk Management and Insurance Review, 14 (2011), 73-88.
doi: 10.1111/j.1540-6296.2011.01195.x. |
| [23] |
S. Sancho, D. Mario, J. Maria, P. Fernando and B. Carlos,
Feature selection methods involving support vector machines for prediction of insolvency in non-life insurance companies, Intelligent Systems in Accouting, Finance and Management, 12 (2004), 261-281.
|
| [24] |
K. Schittkowski,
Optimal parameter selection in support vector machines, Journal of Industrial and Management Optimization, 1 (2005), 465-476.
doi: 10.3934/jimo.2005.1.465. |
| [25] |
B. Scholkopf and A. Smola,
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, 2002. |
| [26] |
K. Shin, T. Lee and H. Kim,
An application of support vector machines in bankruptcy prediction model, Expert Systems and Applications, 28 (2005), 127-135.
doi: 10.1016/j.eswa.2004.08.009. |
| [27] |
N. Siddiqi,
Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring, John Wiley & Sons, 2015.
doi: 10.1002/9781119201731. |
| [28] |
Y. Tian, M. Sun, Z. Deng, J. Luo and Y. Li,
A new fuzzy set and non-kernel svm approach for mislabeled binary classification with applications, IEEE Transactions on Fuzzy Systems, 25 (2017), 1536-1545.
|
| [29] |
V. Vapnik,
Statistical Learning Theory, John Wiley & Sons, Inc., New York, 1998. |
| [30] |
C. Wu, G. Tzeng, Y. Goo and W. Fang,
A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy, Expert Systems with Applications, 32 (2007), 397-408.
doi: 10.1016/j.eswa.2005.12.008. |
| [31] |
C. Xie, C. Luo and X. Yu,
Financial distress prediction based on svm and mda methods: The case of chinese listed companies, Quality & Quantity, 45 (2011), 671-686.
doi: 10.1007/s11135-010-9376-y. |
| [32] |
Z. Yang, W. You and G. Ji,
Using partial least squares and support vector machines for bankruptcy prediction, Expert Systems with Applications, 38 (2011), 8336-8342.
doi: 10.1016/j.eswa.2011.01.021. |
| [33] |
L. Zhang and N. Nielson,
Solvency analysis and prediction in property casualty insurance: Incorporating economic and market predictors, Journal of Risk and Insurance, 82 (2015), 97-124.
|
show all references
References:
| [1] |
M. Al-Smadi,
Credit Risk, Macroeconomic and Bank Specific Factors, VDM Verlag Dr. M$ü$ller, 2011. |
| [2] |
E. Baranoff, T. Sager and T. Shively,
A semiparametric stochastic spline model as a managerial tool for potential insolvency, Journal of Risk and Insurance, 67 (2000), 369-396.
doi: 10.2307/253834. |
| [3] |
A. Best,
Best's insolvency study-property/casualty insurers, Best's Review-Property/Casualty Insurance Edition, (1991), 16-23.
|
| [4] |
J. Carson and R. Hoyt,
Life insurer financial distress: Classification models and empirical evidence, Journal of Risk and Insurance, 62 (1995), 764-775.
doi: 10.2307/253595. |
| [5] |
J. Cheng and M. Weiss,
The fole of rbc, hurricane exposure, bond portfolio duration, and macroeconomic and industry-wide factors in property-liability insolvency prediction, Journal of Risk and Insurance, 79 (2012), 723-750.
|
| [6] |
H. Chew and C. Lim,
On regularisation parameter transformation of support vector machines, Journal of Industrial and Management Optimization, 5 (2009), 403-415.
doi: 10.3934/jimo.2009.5.403. |
| [7] |
S. Cho, J. Kim and J. Bae,
An integrative model with subject weight based on neural network learning for bankruptcy prediction, Expert Systems with Applications, 36 (2009), 403-410.
doi: 10.1016/j.eswa.2007.09.060. |
| [8] |
J. Cummins, M. Grace and R. Phillips,
Regulatory solvency prediction in property-liability insurance: Risk-based capital, audit ratios, and cash flow simulation, Journal of Risk and Insurance, 66 (1999), 417-458.
doi: 10.2307/253555. |
| [9] |
U. Dellepiane, M. Marcantonio, E. Laghi and S. Renzi,
Bankruptcy prediction using support vector machines and feature selection during the recent financial crisis, International Journal of Economics and Finance, 7 (2015), 182-194.
doi: 10.5539/ijef.v7n8p182. |
| [10] |
A. Dimitras, R. Slowinski, R. Susmaga and C. Zopounidis,
Business failure using rough set, European Journal of Operational Research, 114 (1998), 263-280.
|
| [11] |
P. Du Jardin,
Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy, Neurocomputing, 73 (2010), 2047-2060.
doi: 10.1016/j.neucom.2009.11.034. |
| [12] |
M. Grace, S. Harrington and R. Klein,
Risk-based captial and solvency screening in property-liability insurance: Hypothesis and empirical tests, Journal of Risk and Insurance, 65 (1998), 213-243.
|
| [13] |
M. Grant and S. Boyd,
Cvx: Matlab Software for Disciplined Programming, version 1.2, Technical report, http://cvxr.com/cvx 2010. |
| [14] |
S. Hsiao and T. Whang,
A study of financial insolvency prediction model for life insurers, Expert Systems with Applications, 36 (2009), 6100-6107.
doi: 10.1016/j.eswa.2008.07.024. |
| [15] |
M. Kim and D. Kang,
Ensemble with neural networks for bankruptcy prediction, Expert Systems with Applications, 37 (2010), 3373-3379.
doi: 10.1016/j.eswa.2009.10.012. |
| [16] |
G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui and M. Jordan,
Learning the kernel matrix with semi-definite programming, Journal of Machine Learning Research, 5 (2004), 27-72.
|
| [17] |
S. Lee and J. Urrutia,
Analysis and prediction of insolvency in the property-liability insurance industry: A comparison of logit and hazard models, Journal of Risk and Insurance, 63 (1996), 121-130.
doi: 10.2307/253520. |
| [18] |
J. Luo, S.-C. Fang, Y. Bai and Z. Deng,
Fuzzy quadratic surface support vector machine based on fisher discriminant analysis, Journal of Industrial and Management Optimization, 12 (2016), 357-373.
doi: 10.3934/jimo.2016.12.357. |
| [19] |
J. Maria, S. Sanch and B. Carlos,
Prediction of insolvency in non-life insurance companies using support vector machines, genetic algorithms and simulated annealing, Fuzzy Economic Review, 9 (2004), 79-94.
|
| [20] |
J. Min and Y. Lee,
Bankrupty prediction using support vector machine with optimal choice of kernel function parameters, Expert Systems with Applications, 28 (2005), 603-614.
|
| [21] |
S. Min, J. Lee and I. Han,
Hybrid genetic algorithms and support vector machines for bankruptcy prediction, Expert Systems with Applications, 31 (2006), 652-660.
doi: 10.1016/j.eswa.2005.09.070. |
| [22] |
S. Pottier and D. Sommer,
Empirical evidence on the value of group-level financial information in insurer solvency surveillance, Risk Management and Insurance Review, 14 (2011), 73-88.
doi: 10.1111/j.1540-6296.2011.01195.x. |
| [23] |
S. Sancho, D. Mario, J. Maria, P. Fernando and B. Carlos,
Feature selection methods involving support vector machines for prediction of insolvency in non-life insurance companies, Intelligent Systems in Accouting, Finance and Management, 12 (2004), 261-281.
|
| [24] |
K. Schittkowski,
Optimal parameter selection in support vector machines, Journal of Industrial and Management Optimization, 1 (2005), 465-476.
doi: 10.3934/jimo.2005.1.465. |
| [25] |
B. Scholkopf and A. Smola,
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, 2002. |
| [26] |
K. Shin, T. Lee and H. Kim,
An application of support vector machines in bankruptcy prediction model, Expert Systems and Applications, 28 (2005), 127-135.
doi: 10.1016/j.eswa.2004.08.009. |
| [27] |
N. Siddiqi,
Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring, John Wiley & Sons, 2015.
doi: 10.1002/9781119201731. |
| [28] |
Y. Tian, M. Sun, Z. Deng, J. Luo and Y. Li,
A new fuzzy set and non-kernel svm approach for mislabeled binary classification with applications, IEEE Transactions on Fuzzy Systems, 25 (2017), 1536-1545.
|
| [29] |
V. Vapnik,
Statistical Learning Theory, John Wiley & Sons, Inc., New York, 1998. |
| [30] |
C. Wu, G. Tzeng, Y. Goo and W. Fang,
A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy, Expert Systems with Applications, 32 (2007), 397-408.
doi: 10.1016/j.eswa.2005.12.008. |
| [31] |
C. Xie, C. Luo and X. Yu,
Financial distress prediction based on svm and mda methods: The case of chinese listed companies, Quality & Quantity, 45 (2011), 671-686.
doi: 10.1007/s11135-010-9376-y. |
| [32] |
Z. Yang, W. You and G. Ji,
Using partial least squares and support vector machines for bankruptcy prediction, Expert Systems with Applications, 38 (2011), 8336-8342.
doi: 10.1016/j.eswa.2011.01.021. |
| [33] |
L. Zhang and N. Nielson,
Solvency analysis and prediction in property casualty insurance: Incorporating economic and market predictors, Journal of Risk and Insurance, 82 (2015), 97-124.
|
Figure 2.
ROC curves for one test in case C2
Figure 3.
ROC curves for one test in case C3
Table 1.
The firm-level explanatory variables for non-life insurers
| Index | Figure | Index | Ratio |
| F1 | Surplus | R1 | Net Premium / Surplus |
| F2 | Net Technical Reserves | R2 | Tech. Res. / Net Premium |
| F3 | Total Other Liabilities | R3 | Tech. Res. / Surplus |
| F4 | Total Liabilities | R4 | Liq. Assets / Tech. Res.+ Oth. Liabs |
| F5 | Total Investments | R5 | Combined Ratio |
| F6 | Total Other Assets | R6 | Expense Ratio |
| F7 | Total Assets | R7 | Loss Ratio |
| F8 | Gross Premium Written | R8 | Investment Yield |
| F9 | Net Premium Written | R9 | Pre-Tax Profitability |
| F10 | Net Premium Earned | R10 | Liq. Assets/Net Tech. Res. |
| F11 | Underwriting Expenses | R11 | Liq. Ast+Debts ced Co/Net Tech. Res.+Oth Liabs |
| F12 | Underwriting Result | R12 | Profit Bef. Tax / Net Prem. Written |
| F13 | Net Investment Income | R13 | Gross Premium/Surplus |
| F14 | Profit Before Tax | R14 | Change in Surplus |
| F15 | Syndicate Profit | R15 | Change in Technical Reserves |
| F16 | Profit After Tax | R16 | Change in Net Premiums Written |
| F17 | Profit After Names Expenses |
| Index | Figure | Index | Ratio |
| F1 | Surplus | R1 | Net Premium / Surplus |
| F2 | Net Technical Reserves | R2 | Tech. Res. / Net Premium |
| F3 | Total Other Liabilities | R3 | Tech. Res. / Surplus |
| F4 | Total Liabilities | R4 | Liq. Assets / Tech. Res.+ Oth. Liabs |
| F5 | Total Investments | R5 | Combined Ratio |
| F6 | Total Other Assets | R6 | Expense Ratio |
| F7 | Total Assets | R7 | Loss Ratio |
| F8 | Gross Premium Written | R8 | Investment Yield |
| F9 | Net Premium Written | R9 | Pre-Tax Profitability |
| F10 | Net Premium Earned | R10 | Liq. Assets/Net Tech. Res. |
| F11 | Underwriting Expenses | R11 | Liq. Ast+Debts ced Co/Net Tech. Res.+Oth Liabs |
| F12 | Underwriting Result | R12 | Profit Bef. Tax / Net Prem. Written |
| F13 | Net Investment Income | R13 | Gross Premium/Surplus |
| F14 | Profit Before Tax | R14 | Change in Surplus |
| F15 | Syndicate Profit | R15 | Change in Technical Reserves |
| F16 | Profit After Tax | R16 | Change in Net Premiums Written |
| F17 | Profit After Names Expenses |
Table 2.
A confusion matrix
| Observation | Total | |||
| Good | Bad | |||
| Prediction | Good | Correct goods (true negative) | Type Ⅱ error (false negative) | Goods predicted |
| Prediction Bad | Type Ⅰ error (false positive) | Correct bads (true positive) | Bads predicted | |
| Total | Goods observed | Bads observed | Sample size | |
| Observation | Total | |||
| Good | Bad | |||
| Prediction | Good | Correct goods (true negative) | Type Ⅱ error (false negative) | Goods predicted |
| Prediction Bad | Type Ⅰ error (false positive) | Correct bads (true positive) | Bads predicted | |
| Total | Goods observed | Bads observed | Sample size | |
Table 3.
Overall accuracy results for different methods in various cases
| Case | Methods | |||||
| ANN | SVM | MDA | LRA | SLR | FQSSVM | |
| C1 | 0.816 | 0.861 | 0.828 | 0.831 | 0.833 | 0.865 |
| C2 | 0.837 | 0.881 | 0.862 | 0.864 | 0.850 | 0.888 |
| C3 | 0.862 | 0.903 | 0.840 | 0.848 | 0.851 | 0.915 |
| Case | Methods | |||||
| ANN | SVM | MDA | LRA | SLR | FQSSVM | |
| C1 | 0.816 | 0.861 | 0.828 | 0.831 | 0.833 | 0.865 |
| C2 | 0.837 | 0.881 | 0.862 | 0.864 | 0.850 | 0.888 |
| C3 | 0.862 | 0.903 | 0.840 | 0.848 | 0.851 | 0.915 |
Table 4.
Type Ⅰ errors for different methods in various cases
| Case | Type Ⅰ error | |||||
| ANN | SVM | MDA | LRA | SLR | FQSSVM | |
| C1 | 0.185 | 0.141 | 0.173 | 0.170 | 0.168 | 0.137 |
| C2 | 0.165 | 0.121 | 0.139 | 0.137 | 0.151 | 0.115 |
| C3 | 0.139 | 0.099 | 0.161 | 0.154 | 0.151 | 0.088 |
| Case | Type Ⅰ error | |||||
| ANN | SVM | MDA | LRA | SLR | FQSSVM | |
| C1 | 0.185 | 0.141 | 0.173 | 0.170 | 0.168 | 0.137 |
| C2 | 0.165 | 0.121 | 0.139 | 0.137 | 0.151 | 0.115 |
| C3 | 0.139 | 0.099 | 0.161 | 0.154 | 0.151 | 0.088 |
Table 5.
Type Ⅱ errors for different methods in various cases
| Case | Type Ⅱ error | |||||
| ANN | SVM | MDA | LRA | SLR | FQSSVM | |
| C1 | 0.174 | 0.117 | 0.159 | 0.155 | 0.153 | 0.108 |
| C2 | 0.135 | 0.094 | 0.128 | 0.119 | 0.136 | 0.079 |
| C3 | 0.127 | 0.068 | 0.146 | 0.130 | 0.128 | 0.047 |
| Case | Type Ⅱ error | |||||
| ANN | SVM | MDA | LRA | SLR | FQSSVM | |
| C1 | 0.174 | 0.117 | 0.159 | 0.155 | 0.153 | 0.108 |
| C2 | 0.135 | 0.094 | 0.128 | 0.119 | 0.136 | 0.079 |
| C3 | 0.127 | 0.068 | 0.146 | 0.130 | 0.128 | 0.047 |
Table 6.
AUC results for different methods in different comparisons
| Case | Methods | |||||
| ANN | SVM | MDA | LRA | SLR | FQSSVM | |
| C1 | 0.832 | 0.861 | 0.839 | 0.835 | 0.829 | 0.856 |
| C2 | 0.834 | 0.873 | 0.841 | 0.853 | 0.862 | 0.887 |
| C3 | 0.840 | 0.882 | 0.844 | 0.851 | 0.856 | 0.892 |
| Case | Methods | |||||
| ANN | SVM | MDA | LRA | SLR | FQSSVM | |
| C1 | 0.832 | 0.861 | 0.839 | 0.835 | 0.829 | 0.856 |
| C2 | 0.834 | 0.873 | 0.841 | 0.853 | 0.862 | 0.887 |
| C3 | 0.840 | 0.882 | 0.844 | 0.851 | 0.856 | 0.892 |
Table 7.
Computational times (in seconds) for different methods in different comparisons
| Case | Methods | |||||
| ANN | SVM | MDA | LRA | SLR | FQSSVM | |
| C1 | 201.6 | 347.9 | 37.17 | 40.05 | 213.8 | 110.3 |
| C2 | 214.3 | 336.2 | 36.91 | 39.28 | 195.1 | 98.65 |
| C3 | 196.8 | 329.5 | 36.85 | 39.44 | 187.6 | 91.41 |
| Case | Methods | |||||
| ANN | SVM | MDA | LRA | SLR | FQSSVM | |
| C1 | 201.6 | 347.9 | 37.17 | 40.05 | 213.8 | 110.3 |
| C2 | 214.3 | 336.2 | 36.91 | 39.28 | 195.1 | 98.65 |
| C3 | 196.8 | 329.5 | 36.85 | 39.44 | 187.6 | 91.41 |
Table 8.
Main pros and cons of different methods
| Method | Pros | Cons |
| ANN | handle nonlinear structure adaptability to environments resistance to noise pattern recognition | black-box character difficult to interpret hard to analyze results parameters choice |
| SVM | handle nonlinear structure distribution-free good performance | kernel function choice parameters choice computational times |
| MDA | easy to implement computational times | can't handle nonlinear structure strong assumption on data |
| LRA | easy to implement computational times | can't handle nonlinear structure strong assumption on data |
| SLR | easy to implement a proper subset of variables | can't handle nonlinear structure strong assumption on data computational times |
| FQSSVM | handle nonlinear structure no need for kernel function distribution-free good performance good generalization | dimension expansion |
| Method | Pros | Cons |
| ANN | handle nonlinear structure adaptability to environments resistance to noise pattern recognition | black-box character difficult to interpret hard to analyze results parameters choice |
| SVM | handle nonlinear structure distribution-free good performance | kernel function choice parameters choice computational times |
| MDA | easy to implement computational times | can't handle nonlinear structure strong assumption on data |
| LRA | easy to implement computational times | can't handle nonlinear structure strong assumption on data |
| SLR | easy to implement a proper subset of variables | can't handle nonlinear structure strong assumption on data computational times |
| FQSSVM | handle nonlinear structure no need for kernel function distribution-free good performance good generalization | dimension expansion |
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