Advances in Operations Research / 2019 / Article / Tab 8

Review Article

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] 74.3089.10
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] 77.3488.27
Huang et al. [64]79.4989.17
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]79.8889.45
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] 77.7689.33

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]82.8090.75
Wang et al. [55]78.53 86.96
Huang and Wu [83] - 87.54
Oreski and Oreski [86]78.90 -
Krishnaveni et al. [90]80.4093.50
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]81.3087.40
Hsu et al. [53]84.0092.75
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

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