Table 1 Hyperparameters of the KBC algorithm
parameter | Domain | Description |
|---|---|---|
\(M\) | \(\in \mathbb{N}\) | Number of initial base learners |
\(f\) | \(\in \mathbb{N}\) | Number of features to be selected randomly for each base learner |
\(n\) | \(\in \mathbb{N}\) | Number of close neighbors to a new instance (similar instances) |
\({W}^{oob}\) | \(\left[\text{0,1}\right]\) | Weight of OOB instances (default = 0.632). |
\(\delta\) | \(\left[\text{0,1}\right]\) | Minimum acceptance threshold for a base learner’s score to be selected in a neighborhood of a new data point. |