Research Article
A Novel Ensemble Credit Scoring Model Based on Extreme Learning Machine and Generalized Fuzzy Soft Sets
Table 2
Characteristics of data sets.
| Data sets | Size | Attributes | Good/bad | Imbalance ratio | Number of classifiers |
| Credit scoring data sets | | | | | | Germany | 1000 | 25 | 700/300 | 2.33 | 3 | Australia | 690 | 14 | 307/383 | 0.80 | 1 | Japan | 690 | 15 | 296/357 | 0.83 | 1 | Iran | 1000 | 27 | 950/50 | 19 | 19 | Bene 1 | 3,123 | 33 | 2,082/1,041 | 2 | 2 | Bene 2 | 7,190 | 33 | 5,033/2,157 | 2.33 | 3 | |
| Additional imbalanced data sets | | | | | | Shuttle | 12,380 | 9 | 11,428/952 | 12.004 | 13 | Skin_segment | 117,728 | 3 | 114,039/3,679 | 30.99 | 31 | MiniBooNE | 201,355 | 10 | 196,555/4,800 | 40.95 | 41 | LC2017Q1 | 95,633 | 72 | 94,414/1,219 | 77.45 | 78 |
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