Table 1 Machine learning methods used in the experiments with the optional abbreviations used in further work
From: The influence of the inactives subset generation on the performance of machine learning methods
Classifier | Classification scheme | Parameters |
|---|---|---|
Naïve Bayes (NB) | bayes | - |
Sequential Minimal Optimization (SMO) | functions | The complexity parameter was set at 1, the epsilon for a round-off error was 1.0 E-12, and an option of normalizing training data was chosen. |
Kernels: | ||
1) The normalized polynomial kernel, | ||
2) The polynomial kernel | ||
3) The RBF kernel | ||
Instance-Based Learning (Ibk) | lazy | The brute force search algorithm for nearest neighbour search with Euclidean distance function. |
The number of neighbours used: | ||
1) 1 | ||
2) 5 | ||
3) 10 | ||
4) 20 | ||
Decorate | meta | One artificial example used during training, number of member classifiers in the Decorate ensemble: 10, the maximum number of iterations: 10. |
Base classifiers: | ||
1) NaïveBayes | ||
2) J48 | ||
Hyperpipes | misc | - |
J48 | trees | 1) With reduced-error pruning |
2) With C.4.5 pruning | ||
Random Forest (RF) | trees | Trees with unlimited depth, seed number: 1. |
Number of generated trees: | ||
1) 5 | ||
2) 10 | ||
3) 50 | ||
4) 100 |