# An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification
## 4 Simulation Results
### Case 1: UCI Datasets' Classification
#### 4.1.4 Classification on the Heart Dataset  
To evaluate the GA-BEL on the relatively small-sized and high dimensional datasets, we chose the Heart dataset for the test. The configuration in the test changed with every dataset. This is because the attributes of the datasets are different. The Heart dataset has 270 samples with 13 features. According to the features of the Heart dataset, the input nodes, hidden nodes and output nodes are respectively set to 13, 6, 2 after many tests. The initialization of the weights and biases is chosen randomly within the range of [\(-\)1, 1]. For GA, the population size was set to 29 based on the chromosome encoding defined in Equation (8). The Heart dataset has 13 features. Thus, the population size was set to 29 (\(2\times 13+3=29\)). 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 were set to 800 and 700, respectively. The crossover probability and mutation probability were set to 0.7 and 0.02, respectively. We used 70% of samples as training data, and the remaining 30% of samples were used to serve the validation and test purposes. The simulation results are shown in Figure 8.  
Figure 8a 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 1-700 and exhibits no  
Figure 8: Simulation results of the GA-BEL on the Heart dataset. (**a**) Fitness curve during evolution; (**b**) classification confusion matrix for training and testing samples.  
significant improvements after Iteration 600, eventually stopping at Generation 700. This phenomenon demonstrates that GA-BEL comes to convergence after 700 generations and obtains the best chromosome by the evolution.  
From the confusion matrix in Figure 8b, we can see that the classification accuracy (Row 3-Column 3) is 86.2% and 88.9% 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.  
For comparison, we conducted the comparative study between GA-BEL and BEL on the Heart dataset. Fifty trials have been conducted for each algorithm, and 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 were significant, a statistical validation based on the Kruskal-Wallis (K-W) test 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 3.  
As illustrated in Table 2, the results obtained from GA-BEL outperform BEL. The average accuracy has been improved by 2.3%; the resultant \(p\)-value by the K-W test is 0.0258; 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.