Advances in Operations Research / 2019 / Article / Tab 1

Review Article

Credit Scoring: A Review on Support Vector Machines and Metaheuristic Approaches

Table 1

Summary of literature for SVM models.

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

We are committed to sharing findings related to COVID-19 as quickly as possible. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Review articles are excluded from this waiver policy. Sign up here as a reviewer to help fast-track new submissions.