# An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification
## 4 Simulation Results
### Case 1: UCI Datasets' Classification
#### 4.1.3.1 Classification on Breast Cancer Dataset  
The detailed parameter settings in GA-BEL are separate for BEL and GA. For the BEL network, the input patterns determine the number of input neurons, and the classes determines the number of output neurons. Therefore, for the Breast Cancer dataset, the number of the input nodes and output nodes is set to nine and two, respectively. The number of hidden nodes is set to six after many tests. The initialization of the weights and biases is chosen randomly within the range of \([-1,1]\). For GA, the value of the population size is based on the chromosome encoding defined in Equation (8), in which the number of genes in each chromosome is \(2m+3\); \(m\) is the number of features. The Breast Cancer dataset has nine features. Thus, the population size was set to 21 (\(2\times 9+3=21\)). Other parameters' settings were based on the methods related to artificial neural network optimized by GA [25; 26]. Finally, we obtained the best configuration after many tests. Here, the population groups and maximal generation are set to 200 and 100, respectively. The crossover probability and mutation probability are set to 0.8 and 0.03, respectively. Seventy percent of samples are used as training data, and the remaining 30% of samples are used for validation and test purposes. The simulation results are shown in Figure 7.  
Figure 7a shows the best and mean fitness corresponding to each generation during the evolution. It can be observed that the fitness curves gradually improved from Generation 1-100 and exhibits no significant improvements after Iteration 90, eventually stopping at Generation 100. This phenomenon demonstrates that GA-BEL comes to convergence and obtains the best chromosome by the evolution.  
From the confusion matrix in Figure 7b, we can see that the classification accuracy (Row 3-Column 3) is 96.1% and 97.6% in the training and testing samples, respectively. The precision (Row 1-Column 3) and recall (Row 3-Column 2) of the classification are also given in the two confusion matrices.  
Figure 6: Confusion matrix.  
As described above, the proposed GA-BEL aims to enhance the BEL classification accuracy by optimizing the parameters of weights and biases in the BEL network. For comparison, we conducted the comparative study between GA-BEL and BEL on the Breast Cancer dataset. Fifty trials have been conducted for each algorithm; the results in terms of precision, recall, accuracy and computing time were recorded. To ensure that the improvements obtained by using the proposed GA-BEL are significant, a statistical validation based on the Kruskal-Wallis (K-W) test [28] was performed. Results including the average precision, recall, accuracy, computing time, as well as the \(p\)-value by the K-W test are listed in Table 2.  
As illustrated in Table 2, the results obtained from GA-BEL outperform BEL. The average accuracy has been improved by 2.6%; the resultant \(p\)-value by the K-W test is 0.0183; the value is less than a chosen significance level of 0.05. It can be confirmed that there is statistically significance between the results. The other comparisons in terms of average precision, recall and the computing time are at the statistical significance level of 0.05.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance Metric** & **BEL** & **GA-BEL** & **Paired K-W Test \(p\)-Value** \\ \hline Precision (\%) & 97.3 & **98.5** & 0.0356 \\ Recall (\%) & 94.5 & **95.6** & 0.0202 \\ Accuracy (\%) & 94.9 & **97.5** & 0.0183 \\ Time (s) & \(8.27\times 10^{-3}\) & \(7.63\times 10^{-3}\) & 0.0316 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of BEL vs. GA-BEL on the Breast Cancer dataset.  
Figure 7: Simulation results of the GA-BEL on the Breast Cancer dataset. (**a**) Fitness curve during evolution; (**b**) classification confusion matrix for training and testing samples.