Research Article
Feature Selection for Better Identification of Subtypes of Guillain-Barré Syndrome
Table 6
Purity for different numbers of clusters using the four GBS subtypes.
| | IG | SU | CFS | Consistency | Chi-squared | All features |
| 2 | 0.6434 (9) | 0.6434 (10) | 0.6511 (16) | 0.4883 (6) | 0.6124 (48) | 0.5038 | 3 | 0.7906 (7) | 0.7906 (7) | 0.7286 (16) | 0.6666 (6) | 0.7829 (6) | 0.5813 | 4 | 0.7984 (7) | 0.7984 (7) | 0.7984 (16) | 0.6589 (6) | 0.7829 (41) | 0.6899 | 5 | 0.7984 (5) | 0.7984 (5) | 0.7906 (16) | 0.7286 (6) | 0.7829 (91) | 0.6821 | 6 | 0.7751 (4) | 0.7751 (7) | 0.8139 (16) | 0.7596 (6) | 0.7751 (38) | 0.6666 | 10 | 0.8139 (5) | 0.8139 (5) | 0.8217 (16) | 0.7596 (6) | 0.8062 (38) | 0.6976 | 20 | 0.8449 (38) | 0.8372 (31) | 0.8527 (16) | 0.8294 (6) | 0.8294 (53) | 0.7596 |
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: number of clusters, IG: information gain, and SU: symmetrical uncertainty. The number of features selected in each case is shown in parenthesis.
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