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) - (i) study effect of misclassification costs (ii) compare with profit analysis and weight-of-evidence measure Dong et al. (2009) [66 ] SA (IB) - accuracy (i) rules extraction (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 (i) features extraction (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 (i) feature selection (wrapper approach) (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 (i) multiclass problem (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
