Research Article
A Predictive Model for Guillain–Barré Syndrome Based on Ensemble Methods
Table 2
Base classifiers used in Random Subspace for each classification case.
| Base classifier | Parameter setting | Classes |
| kNN | k = 18, d = Manhattan | AIDP, AMAN, AMSAN, MF | kNN | k = 18, d = Manhattan | AIDP vs. ALL | kNN | k = 18, d = Manhattan | AMAN vs. ALL | kNN | k = 18, d = Manhattan | AMSAN vs. ALL | Naive bayes | — | MF vs. ALL | yjJRip | NumOpt = 3 | AIDP vs. AMAN | SVMGaus | s = 0.01, C = 10 | AIDP vs. AMSAN | OneR | — | AIDP vs. MF | kNN | k = 18, d = Manhattan | AMAN vs. AMSAN | SVMGaus | s = 0.01, C = 10 | AMAN vs. MF | Naive bayes | — | AMSAN vs. MF |
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