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| Authors | SVM type | Kernel | Other Methods | Remarks |
|
| Standard SVM and Variants | | | | |
| Baesens et al. (2003) [8] | SVM, LS-SVM | linear, RBF | - | (i) compare with LOGIT, DA, kNN, LP, BN, NN, TREE |
| Van Gestel et al. (2003) [19] | LS-SVM | RBF | - | (i) multiclass corporate rating |
| | | | | (ii) compare with LR, LOGIT, NN |
| Huang et al. (2004) [20] | SVM | linear, RBF, polynomial | - | (i) multiclass corporate rating |
| | | | | (ii) compare with NN |
| Li et al. (2006) [21] | SVM | RBF | - | (i) compare with NN |
| | | | | (ii) study misclassification error |
| Lai et al. (2006) [22] | LS-SVM, SVM | RBF | - | (i) multiclass corporate rating |
| | | | | (ii) compare with NN, LR, LOGIT |
| Lee et al. (2007) [23] | SVM | RBF | - | (i) multiclass corporate rating |
| | | | | (ii) compare with NN, DA, CBR |
| Bellotti and Crook (2009) [24] | SVM | linear, RBF, polynomial | - | (i) application on large dataset |
| | | | - | (ii) compare with LOGIT, LR, DA, kNN |
| | | | - | (iii) support vector weights to select significant features |
| Kim and Sohn (2010) [25] | SVM | RBF | - | (i) multiclass corporate rating on SME |
| | | | | (ii) compare with NN, LOGIT |
| Danenas et al. (2011) [26] | SVM | linear, RBF, polynomial, | - | (i) compare between SVMs of different libraries (LIBLINEAR, LIBSVM, |
| | | Laplacian, Pearson, inverse | | WEKA, LIBCVM) |
| | | distance, inverse square | | |
| | | distance | | |
| Lessmann et.al (2015) [11] | SVM | linear, RBF | - | (i) compare with LOGIT, TREE, ELM, kNN, DA, BN, ensembles |
| | | | | (ii) recommendation to use different performance measures |
| Louzada et.al (2016) [12] | SVM | not mentioned | - | (i) compare with LR, NN, TREE, DA, LOGIT, FUZZY, BN, SVM, GP, |
| | | | | hybrid, ensembles |
| | | | | (ii) study class imbalance problem |
| Boughaci and Alkhawaldeh (2018) [18] | SVM | not mentioned | - | (i) compare with kNN, BN, NN, TREE, SVM, LOGIT and ensembles |
| Mushava and Murray (2018) [27] | SVM | RBF | - | (i) compare with LOGIT, DA, extensions of LOGIT and |
| | | | | DA, ensembles |
|
| Modified SVM | | | | |
| Wang et.al (2005) [28] | SVM | linear, RBF, polynomial | fuzzy membership | (i) introduce bilateral weighting error into classification problem |
| | | | | (ii) compare with U-FSVM, SVM, LR, LOGIT and NN |
| Harris (2015) [29] | SVM | linear, RBF | k-means cluster | (i) reduce computational time |
| | | | | (ii) compare with LOGIT, k-means+LOGIT, SVM, k-means+SVM |
| Yang (2017) [30] | WSVM | RBF, KGPF | - | (i) dynamic scoring with adaptive kernel |
| | | | | (ii) ranking of kernel attributes to solve black box model |
| | | | | (iii) compare with LOGIT |
| Li et al. (2017) [31] | L2-SVM | not mentioned | - | (i) reject inference |
| | | | | (ii) compare with LOGIT, SVM |
| Tian et al. (2018) [32] | L2-SVM | no kernel | - | (i) reduce computational time |
| | | | | (ii) reject inference and outlier detection |
| | | | | (iii) compare with LOGIT, kNN, SVM, SSVM |
| Maldonado et al. (2017) [33] | SVM, | linear | - | (i) feature selection |
| | 1-norm SVM | | | (ii) acquisition cost into formulation of SVM |
| | | | | (iii) application and behavioural scoring |
| | | | | (iv) study class imbalance problem |
| | | | | (v) compare with SVM (filter and wrapper feature selection) |
| Maldonado et al. (2017) [34] | SVM, | linear | - | (i) profit-based feature selection |
| | LP-norm SVM | | | (ii) group penalty function included in SVM formulation |
| | | | | (iii) compare with LOGIT, SVM (filter, wrapper feature selection) |
|
| Hybrid SVM | | | | |
| Huang et al. (2007) [35] | SVM | RBF | GA | (i) hyperparameters tuning, features selection (wrapper approach) |
| | | | | (ii)compare with GP, NN, TREE |
| Martens et al. (2007) [36] | SVM | RBF | C4.5, Trepan, | (i) rules extraction |
| | | | G-REX | (ii) compare with LOGIT, SVM, TREE |
| Zhou and Bai (2008) [37] | SVM | RBF | rough sets | (i) features selection (filter approach) |
| | | | | (ii) compare with DA, NN, SVM, SVM wrapped by GA |
| Zhang et al. (2008) [38] | SVM | RBF | GP | (i) rules extraction |
| | | | | (ii) compare with SVM, GP, LOGIT, NN, TREE |
| Yao (2009) [39] | SVM | RBF | neighbourhood | (i) features selection (filter approach) |
| | | | rough set | (ii) compare with DA, LOGIT, NN |
| Xu et al. (2009) [40] | SVM | RBF | link analysis | (i) features extraction with link relation of applicants |
| | | | | (ii) compare with SVM |
| Zhou et al. (2009) [41] | WSVM | linear, RBF | GA | (i) hyperparameters tuning, features selection (wrapper approach) |
| | | | | (ii) features weighting |
| | | | | (iii) compare with LR, LOGIT, NN, TREE, kNN, Adaboost |
| Zhou et al. (2009) [42] | LSSVM | RBF | DS, GA, GS, DOE | (i) hyperparameters tuning (wrapper approach) |
| | | | | (ii) compare with LOGIT, kNN, DA, TREE |
| Chen and Li (2010) [43] | SVM | RBF | DA, TREE, | (i) features selection (filter approach) |
| | | | rough sets, | (ii) compare with SVM |
| | | | F-score | |
| Yu et al. (2010) [44] | WLS-SVM | RBF | DS, GA, GS, DOE | (i) hyperparameters tuning (wrapper approach) |
| | | | | (ii) study class imbalance problem |
| | | | | (iii) compare with results from [8, 45] |
| Hens and Tiwari (2011) [46] | SVM | linear | stratified sampling | (i) reduce computational time |
| | | | | (ii) compare with SVM, NN, GP |
| Danenas and Garsva (2012) [47] | SVM from | linear | PSO, GA | (i) model selection |
| | LIBLINEAR | | | (ii) hyperparameters tuning (wrapper approach) |
| Chen et al. (2012) [48] | SVM | RBF | k-means cluster | (i) reject inference |
| | | | cluster | (ii) multiclass problem with different cutoff points |
| Chen et al. (2013) [49] | SVM | RBF | ABC | (i) hyperparameters tuning (wrapper approach) |
| | | | | (ii) compare with SVM tuned with GA and PSO |
| Han et al. (2013) [50] | SVM | linear | orthogonal dimension | (i) features extraction with dimension reduction |
| | | | reduction | (ii) compare with LOGIT |
| Garsva and Danenas (2014) [51] | LS-SVM, | linear, RBF, polynomial, | PSO, DS, SA | (i) model selection |
| | SVM from | sigmoid | | (ii) hyperparameters tuning (wrapper approach) |
| | LIBLINEAR | | | (iii) study class imbalance problem |
| | | | | (iv) compare among all SVM and LS-SVM tuned with PSO, DS, SA |
| Danenas and Garsva (2015) [52] | SVM from | linear | PSO | (i) model selection |
| | LIBLINEAR | | | (ii) hyperparameters tuning (wrapper approach) |
| | | | | (iii) compare with LOGIT, RBF network classifier, SVM tuned with DS |
| Hsu et al. (2018) [53] | SVM | RBF | ABC | (i) hyperparameters tuning (wrapper approach) |
| | | | | (ii) compare with LOGIT, SVM tuned with GS, GA and PSO |
| Jadhav et al. (2018) [54] | SVM | RBF | GA | (i) features selection (wrapper approach) |
| | | | | (ii) compare with standalone SVM, kNN, NB and their wrappers with GA |
| | | | | with standard GA |
| Wang et al. (2018) [55] | SVM | RBF | multiple population GA | (i) features selection (wrapper approach) |
| | | | | (ii) compare with MPGA-SVM, GA-SVM, SVM |
|
| Ensemble Model | | | | |
| Zhou et al. (2010) [56] | LS-SVM | RBF | fuzzy C-means | (i) homogeneous ensemble |
| | | | | (ii) compare with ensemble and single classifiers |
| Ghodselahi (2011) [57] | SVM | linear, RBF, polynomial, | - | (i) homogeneous ensemble |
| | | sigmoid | | (ii) compare with ensemble and single classifiers |
| Yu et al. (2018) [58] | SVM | RBF | DBN | (i) homogeneous ensemble |
| | | | | (ii) study class imbalance problem |
| | | | | (iii) compare with ensemble and single classifiers |
| Xia et al. (2018) [59] | SVM | RBF | RF, GPC, | (i) heterogeneous ensemble |
| | | | XGBoost | (ii) compare with ensemble and single classifiers |
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