The one that can achieve the highest prediction accuracy or smallest log rank test p-value is considered to be the optimized feature set selected by Incremental Feature Selection (IFS) [14], [15], [19].
To maximize the multiple nodes CR network system, the capacity maximization problem (18) can be rewritten as in (25), in which Opt C i is the optimal channel set selected by STi according to the channel selection criteria.
Our empirical experiment indicates that the prediction model based on the feature set selected by the proposed method outperforms those models based on traditional feature selection methods in terms of prediction accuracy.
All participants saw the same baseline color set, selected using the baseline merit function.
This may indicate that there are some redundancy features in the optimum feature set selected by CCLS method.
The basic idea is to combine the neutral model set of the target speaker and an emotional model set selected from a pool of speakers.
Compared to the optimal parameter set selected from orthogonal array by Taguchi method, the optimal prediction design can receive an improvement of 22.3% in electrical resistivity, and the corresponding resistivity is 8.08 × 10−4 Ω cm.
In the data set selected for the experiments, no instance belonging to novel class was declared as existing class instance in the data set and very few instances belonging to existing class were declared as novel class instance.
This movement depends on the size of MDP(_{mathrm {REF}}) rules set selected in the first iteration, since these selected rules are correlated with the employed and utilized measures.
The total number of reference HMM sets used in the experiment was 4 × 16 = 64, with a single HMM set selected for recognition depending on the noise type and SNR value of the noisy test speech.
The results show that the gene set selected by MOEDA not only results in higher accuracies, but also keep a small scale, which cannot only save computational time but also improve the interpretability and application of the result with the simple classification model.
