Advances in Operations Research / 2019 / Article / Tab 3

Review Article

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

Table 3

Summary of literature for MA.

Authors MA (category) Other Methods fitness function Remarks

Standard MA
Desai et al. (1997) [60] GA (EA) - no. of misclassification(i) multiclass problem
(ii) compare with NN, LOGIT, DA
Ong et al. (2005) [61] GP (EA) - mean absolute error(i) rule extraction
(ii) compare with LOGIT, NN, TREE, rough sets
Finlay (2006) [62] GA (EA) - GINI coefficient(i) large credit data application
(ii) compare with LOGIT, LR, NN
Cai et al. (2009) [63] GA (EA) - error rate(i) include misclassification cost into fitness
function
Huang et al. (2006) [64] 2stage GP (EA) - mean absolute error(i) rules extraction
(ii) compare with LOGIT, TREE, kNN, GP
Abdou et al. (2009) [65] GP (EA) - (sum of square error) +(i) study effect of misclassification costs
(classification error)(ii) compare with profit analysis
and weight-of-evidence measure
Dong et al. (2009) [66] SA (IB) - accuracy similarity function (i) rules extraction
( is a coefficient)(ii) compare with DA, kNN, TREE
Martens et al. (2010) [67] ACO (SI) - coverage + confidence(i) rules extraction
(ii) compare with TREE, SVM, majority vote
Yang et al. (2012) [68] CS (SI) - error rates(i) compare with Altman’s Z-score and SVM
Uthayakumar et al. (2017) [69] ACO (SI) -(i) rules extraction
(ii) compare with LOGIT, NN, RF, RBF network

Hybrid MA-MA
Jiang et al. (2011) [70] SA+GA NN(i) SA optimizes GA
(IB+EA)(ii) parameter tuning
(iii) compare with standalone NN and GA-optimized NN
Aliehyaei and Khan (2014) [71] ACO (SI), GP (EA), - mean absolute error(i) rules extraction by ACO to input to GP
ACO+GP(ii) compare with ACO and GP

Hybrid MA-DM
Fogarty and Ireson (1993) [72] GA (EA) BN accuracy(i) features extraction
(ii) large credit data application
(iii) compare with default rule, BN, kNN, TREE
Hand and Adams (2000) [73] SA (IB) WOE, weighted likelihood(i) features discretization
WOE(ii) compare with LOGIT, DA, two other discretization methods
Drezner et al. (2001) [74] TS (IB) LR(i) features selection (wrapper approach)
(ii) compare with Altman’s Z-score
Lacerda et al. (2005) [75] GA (EA) NN average of individuals with(i) hyperparameters tuning
rank of individual(ii) compare with NN, consecutive learning algorithm, SVM
from a population
Huang et al. (2007) [35] GA (EA) SVM accuracy(i) hyperparameters tuning, feature selection (wrapper approach)
(ii) compare with NN, GP, TREE
Zhang et al. (2008) [38] GP (EA) SVM accuracy+expected(i) rules extraction
misclassification cost(ii) compare with SVM, GP, TREE, LOGIT, NN
Liu et al. (2008) [76] GP (EA) DA Information value(i) features extraction (select derived characteristics)
(ii) large dataset from finance enterprise
(iii) compare with DA
Wang et al. (2008) [77] GA (EA) NN 1/MSE(i) parameters tuning
(ii) compare with NN
Zhang et.al (2008) [78] GA (EA) TREE (1-info entropy)+(i) features extraction
(1-info entropy)(ii) compare with TREE, NN, GP, GA-optimized SVM, rough set
Jiang (2009) [79] SA (IB) TREE(i) (i) rules extraction
(ii) (ii) compare with TREE
(iii)
Marinakis et al. (2009) [80] ACO (SI), kNN, 1-NN, not mentioned(i) multiclass problem, features selection (wrapper approach)
PSO (SI) weighted kNN(ii) compare with wrapper models of GA and TS with kNN
(and variants)
Sustersic et al. (2009) [81] GA (EA) NN accuracy and RMSE ,(i) feature selection (wrapper approach)
, preset threshold(ii) compare with NN, LOGIT (features from PCA)
Zhou et al. (2009) [42] GA (EA) SVM error rate(i) hyperparameters tuning
(ii) compare with LOGIT, kNN, DA, TREE
Zhou et al. (2009) [41] GA (EA) WSVM AUC(i) hyperparameters tuning, feature selection (wrapper approach)
(ii) compare with LR, LOGIT, NN, TREE, kNN, Adaboost
Marinaki et al. (2010) [82] HBO (SI) kNN, 1-NN, accuracy(i) multiclass problem, features selection (wrapper approach)
weighted kNN(ii) compare with wrapper models of GA, PSO, TS, ACO with kNN
(and variants)
Huang and Wu (2011) [83] GA (EA) kNN, BN, TREE, LR, accuracy(i) feature selection (wrapper approach)
SVM, NN, Adaboost,(ii) compare with kNN, BN, TREE, LOGIT, SVM (features from
Logitboost, Multiboost filter selection approach)
Yu et al. (2011) [44] GA (EA) LS-SVM accuracy(i) hyperparameters tuning
(ii) study class imbalance problem
(iii) compare with results from [8, 45]
Correa and Gonzalez (2011) [84] BPSO (SI), NN AUC(i) hyperparameters tuning
GA (EA)(ii) large credit dataset
(iii) cost study on application, behavioural and collection scoring
(iv) compare with LOGIT, NN, Global Optimum
Oreski et al. (2012) [85] GA (EA) NN accuracy(i) hyperparameters tuning, feature selection (wrapper approach)
(ii) study effect of misclassification costs
(iii) compare their proposed model with features from
filter selection approach
Danenas and Garsva (2012) [47] GA (EA),PSO (SI) SVM TPR(i) model selection
(ii) hyperparameters tuning
Chen et al. (2013) [49] ABC (SI) SVM not mentioned(i) multiclass problem
(ii) hyperparameters tuning
(iii) compare with SVM (tuned with GA and PSO)
Garsva and Danenas (2014) [51] PSO (SI) SVM (i) TPR, (ii) accuracy(i) model selection
(ii) hyperparameters tuning
(iii) study class imbalance problem
(iv) compare among all SVM, LS-SVM tuned with PSO, DS, SA
Oreski and Oreski (2014) [86] GA (EA) NN accuracy(i) feature selection (wrapper approach)
(ii) compare with standard wrapper-based NN with GA
Danenas and Garsva (2015) [52] PSO (SI) SVM TPR(i) model selection
(ii) hyperparameters tuning
(iii) compare with LOGIT, RBF network
Wang et al. (2010) [87] TS (IB) NN, SVM, LOGIT entropy(i) features selection (filter approach)
(ii) compare with NN, SVM, LOGIT with full features
Wang et al. (2012) [88] SS (SI) NN, TREE, LOGIT entropy(i) features selection (filter approach)
(ii) compare with NN, TREE, LOGIT with full features
Waad et al. (2014) [89] GA (EA) LOGIT, SVM, TREE(i) feature selection (filter approach)
(ii) compare with LOGIT, SVM,TREE (features from other filter
selection and rank aggregation methods)
Hsu et al. (2018) [53] ABC (SI) SVM, if (i) multiclass problem
, if (ii) hyperparameters tuning
(iii) compare with LOGIT and SVM (tuned with GS, GA, PSO)
Jadhav et al. (2018) [54] GA (EA) SVM, kNN, NB accuracy(i) features selection (wrapper approach)
(ii) compare with standalone SVM, kNN, NB and their
wrappers with GA
Wang et al. (2018) [55] multiple population SVM accuracy(i) features selection (wrapper approach)
GA (EA)(ii) compare with MPGA-SVM, GA-SVM, SVM
Krishnaveni et al. (2018) [90] HS (SI) 1-NN accuracy(i) features selection (wrapper approach)
(ii) computational time reduction
(iii) compare with standalone SVM, TREE, kNN, NB, NN
and their wrappers with GA and PSO

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