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

Table 8

Results from literature.

Purpose	Model Type	Authors	G	A
Investigation of predictive	standard SVM and	Baesens et al. [8]	74.30	89.10
ability	its variants	Lessmann et al. [11]	75.30	86.00
		Boughaci and Alkhawaldeh [18]	69.90	80.70
	standalone MA	Cai et al. [63]	80.00	-
Computational efficiency	modified SVM	Harris [29]	77.10	-
	hybrid SVM	Hens and Tiwari [46]	75.08	85.98
Improvement of classfication	ensembles	Zhou et al. [56]	78.13	-
performance		Ghodselahi [57]	81.42	-
		Xia et al. [59]	78.32	86.29
Rules extraction	hybrid SVM	Martens et al. [36]	-	85.10
	standalone MA	Ong et al. [61]	77.34	88.27
		Huang et al. [64]	79.49	89.17
		Dong et al. [66]	72.90	-
		Martens et al. [67]	80.80	-
		Uthayakumar et al. [69]	-	86.37
	hybrid MA-MA	Aliehyaei and Khan [71]	70.70	84.30
	hybrid MA-DM	Zhang et al. [38]	79.88	89.45
		Jiang et al. [79]	73.10	-
Features extraction	hybrid SVM	Xu et al. [40]	-	89.28
		Han et al. [50]	75.00	-
	hybrid MA-DM	Zhang et al. [78]	77.76	89.33
Features selection	hybrid SVM	Yao [39]	76.60	87.52
		Chen and Li [43]	76.70	86.52
	hybrid MA-DM	Jadhav et al. [54]	82.80	90.75
		Wang et al. [55]	78.53	86.96
		Huang and Wu [83]	-	87.54
		Oreski and Oreski [86]	78.90	-
		Krishnaveni et al. [90]	80.40	93.50
		Wang et al. [88]	-	88.90
Hyperparameters tuning	hybrid MA-DM	Zhou et al. [42]	77.10	86.96
		Yu et al. [44]	78.46	90.63
		Garsva and Danenas [51]	81.30	87.40
		Hsu et al. [53]	84.00	92.75
		Lacerda et al. [75]	-	86.05
Simultaneous features	hybrid MA-DM	Huang et al. [35]	77.92	86.90
selection & hyperparameters
tuning
		mean	77.56	87.75
		standard deviation	3.35	2.64