|
| 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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