## 5 Simulation Experiments and Result Analysis
### Simulation Experiments on UCI Standard Datasets
The layer number set to be 8, and the numbers of hidden neural nodes were set to be [15; 12; 12; 10; 10; 8; 8]/[35; 24; 24; 17; 15; 15; 15; 15; 15; 12; 18; 15; 12; 12; 12] for three UCI standard datasets in Deep Learning algorithm with Sigmodal function as the activation function. In the proposed Peak Clustering and GANs-HDI algorithms, the threshold of discrimination rate was set to be \(c=0.05\), the reducing pace was set to be \(\alpha=0.0015\), the numbers of hidden neural nodes were set to be \(L=10/25/12\), initialized threshold values as \(R_{1}=0.85\) and \(R_{2}=0.6\), clustering thresholds as \(k=2\), select Sigmodal function and new generation proportion \(\overline{N}/N=0.5\). Repair the incomplete data samples with k-NN, Peak Clustering, BP, Matrix Completion [14], Deep Learning [18], and GANs-HDI algorithms, respectively. Then, determine whether the categories of restored samples were correct or not by using support vector machine (SVM), and calculate the accuracy values. Repeat 10 trials independently, and calculate the averages and root mean squared error (RMSE) of the accuracy values of data restoration results, as shown in Table 4 (more details in Table 4). According to the data shown in Table 4, it is obvious that the time-consuming of both k-NN, matrix completion, Peak Clustering algorithms held the almost same quantity level, while Peak Clustering performed much better than the traditional k-NN algorithm and matrix completion algorithm on the accuracy of data restoration, especially held a more prominent repair effect for linear inseparable data samples. The data restoration performances of BP and Deep Learning [18] algorithm succeeded to beat Peak Clustering algorithm on UCI datasets. However, its RMSE was far from requirement, so the BP algorithm is not stable enough to carry out the engineering application directly. It is worth noting that the GANs-HDI algorithm is far superior to the other control groups in both of the accuracy and RMSE with 20-35 percentage points ahead. However, the algorithm takes a longer time to run, and needs to rely on regularization constraints and distributed computing technologies to improve its convergence efficiency. In summary, the data restoration performances of GANs-HDI on UCI standard datasets were outstanding when compared with k-NN, BP, matrix completion, Peak Clustering, and deep learning \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Index**} & \multicolumn{3}{c}{**Dataset**} \\ \cline{3-5}  & & **Abalone** & **Heart Disease** & **Bank Marketing** \\ \hline \multirow{3}{*}{k-NN} & Accuracy/\% & 42.87 & 33.33 & 60.02 \\  & RMSE & / & / & / \\  & Time-consuming/s & 7.2130 & 2.1621 & 50.1315 \\ \hline \multirow{3}{*}{Peak Clustering} & Accuracy/\% & 58.56 & 43.33 & 67.92 \\  & RMSE & 0.013379 & 0.022361 & 0.018203 \\  & Time-consuming/s & 9.1655 & 2.3097 & 76.1617 \\ \hline \multirow{3}{*}{Matrix Completion [14]} & Accuracy/\% & 53.27 & 40.21 & 61.23 \\  & RMSE & 0.075152 & 0.065465 & 0.116122 \\  & Time-consuming/s & 17.5660 & 10.6516 & 226.6646 \\ \hline \multirow{3}{*}{BP} & Accuracy/\% & 62.46 & 50.17 & 64.26 \\  & RMSE & 0.039062 & 0.135082 & 0.037608 \\  & Time-consuming/s & 17.2132 & 8.1261 & 180.2661 \\ \hline \multirow{3}{*}{Deep Learning [18]} & Accuracy/\% & 71.26 & 66.02 & 69.95 \\  & RMSE & 0.036261 & 0.0461610 & 0.091664 \\  & Time-consuming/s & 140.5594 & 82.6167 & 362.9500 \\ \hline \multirow{3}{*}{GANs-HDI} & Accuracy/\% & 94.24 & 69.17 & 89.72 \\  & RMSE & 0.014235 & 0.018634 & 0.001142 \\ \cline{1-1}  & Time-consuming/s & 154.1288 & 89.2661 & 1374.1626 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of four algorithms on UCI datasets. algorithms. The validity and stability of our proposed GANs-HDI algorithm are verified through the simulation comparison experiments. Furthermore, the experimental results from UCI standard data also showed that the sample number might have a great influence on the final performances of the GANs-HDI algorithm.