Table 4 The best values of the parameters obtained by the PSO evolutionary algorithm
Hyper parameters | Value | Definition |
|---|---|---|
\(R_{ + }\) | 20 | It is evident that by choosing a low value for \(R_{ + }\), the optimum solution for \(\sigma\) would include more zero values and consequently it results in a higher feature reduction rate |
\(R_{ - }\) | 50 | It is evident that by choosing a low value for \(R_{ - }\), the optimum solution for \(\sigma\) would include more zero values and consequently it results in a higher feature reduction rate |
C | 0.1 | The non-negative parameter \(C\) indicated the penalty for the regularization term |
\(\beta\) | 0.0001 | The parameter \(\beta\) indicates the importance level of the second term in the equation |
\(k_{1}\) | 3 | The number of neighbors are considered |
\(k_{2}\) | 3 | The number of neighbors are considered |
\(max\;iteration\) | 10 | Repeat of optimization steps |