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

Table 9

Results categorized with data splitting methods.

Data Split	Authors	G	A
k-fold cv	Dong et al. [66]	72.90	-
	Uthayakumar et al. [69]	-	86.37
	Zhang et al. [38]	79.88	89.45
	Xu et al. [40]	-	89.28
	Han et al. [50]	75.00	-
	Zhang et al. [78]	77.76	89.33
	Yao [39]	76.60	87.52
	Chen and Li [43]	76.70	86.52
	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	-
	Wang et al. [88]	-	88.90
	Zhou et al. [42]	77.10	86.96
	Yu et al. [44]	78.46	90.63
	Hsu et al. [53]	84.00	92.75
	Huang et al. [35]	77.92	86.90
	mean	78.20	88.56
	standard deviation	2.93	1.92
holdout	Baesens et al. [8]	74.30	89.10
	Cai et al. [63]	80.00	-
	Boughaci and Alkhawaldeh [18]	69.90	80.70
	Harris [29]	77.10	-
	Zhou et al. [56]	78.13	-
	Ghodselahi [57]	81.42	-
	Martens et al. [36]	-	85.10
	Martens et al. [67]	80.80	-
	Aliehyaei and Khan [71]	70.70	84.30
	Garsva and Danenas [51]	81.30	87.40
	mean	77.07	85.32
	standard deviation	4.47	3.20
rep k-fold	Lessmann et al. [11]	75.30	86.00
	Xia et al. [59]	78.32	86.29
	Krishnaveni et al. [90]	80.40	93.50
	mean	78.01	88.60
	standard deviation	2.56	4.25
rep holdout	Ong et al. [61]	77.34	88.27
	Huang et al. [64]	79.49	89.17
	Lacerda et al. [75]	-	86.05
	mean	78.42	87.83
	standard deviation	1.52	1.61