## 6 Comparison of PSO, BA, and DE Regarding Data Classification
### NN Classifications with DE-Based Initialization Approaches
#### Discussion  
The multi-layer feed-forward neural network is trained with the back propagation algorithm, standard DE, DE-S, DE-H, and proposed DE-TO, DE-KN, and DE-WE. For this goal, we prepared the multi-layer feed-forward neural network utilizing the process of weight optimization. The performance of the DE, DE-S, DE-H, DE-TO, DE-KN, and DE-WE and state of the art NN algorithms are examined on 10 well-known datasets which have been taken directly from the worldwide UCI repository of machine learning. The features of those informational indexes are given in Table 10. These features include the total units participated against each dataset, the number of total input instances, the dataset nature, and the number of classes against each dataset i.e., binary class problem or multi-class problem. The impact of increasing the number of target classes is independent as the proposed strategy is purely concerned with weight optimization rather feature selection or reducing high dimensionality. The 10-fold cross validation method has been carried out for the training and testing process. The experimental results of algorithms such as with back propagation, standard DE, DE-S, DE-H, DE-WE, DE-TO, and DE-KN are compared with each other on seven well-known real datasets taken from UCI and their performances are evaluated. In Table 13, the simulation results show that training of neural networks with the DE-H algorithm outperforms in accuracy and is capable of providing the good classification accuracy than the other traditional approaches. The DE-H algorithm may be used effectively for data classification and statistical problems in the future as well. Figure 58 represents the accuracy graph for seven datasets. Figure 57: Multi-comparison post-hoc Tukey test graph of all PSO-based. To prove the experimental results statistically, the testing accuracy of classification datasets were loaded to the software RStudio (1.2.5001 version). The classification results of seven approaches of DE were tested along with the one-way ANOVA statistical test and post-hoc [PERSON]'s pair-wise comparison statistical test [60] using significance level 0.05. The findings of the classification dataset with one-way ANOVA are illustrated in Table 14, where the significance = 0.02043 is less than the above-mentioned threshold of significance level. The findings in Table 14 prove that there are significant dissimilarities in all variants of DE with a 95% confidence level. Figure 59 demonstrates the graph of one-way ANOVA which gives the evidence that H-DE is significantly better than other approaches of DE. Figure 60 models the findings of pairwise comparisons of DE approaches with the post-hoc [PERSON]'s statistical test. The simulated graph describes that the H-DE approach is statistically significant dissimilar as compared to other approaches of DE having a 95% confidence level. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**S. No**} & \multirow{2}{*}{**Data Sets**} & \multirow{2}{*}{**Type**} & **BPA** & **DENN** & **SO-DENN** & **H-DENN** & **TO-DENN** & **KN-DENN** & **WE-DENN** \\ \cline{3-10}  & & & **Ts. Acc** & **Ts. Acc** & **Ts. Acc** & **Ts. Acc** & **Ts. Acc** & **Ts. Acc** & **Ts. Acc** \\ \hline
2 & Diabetes & 2-Class & 65.3\% & 66.1\% & 68.16\% & 69.6\% & 71.30\% & 67.17\% & **75.50\%** \\
3 & Heart & 2-Class & 68.3\% & 70.5\% & 72.5\% & 71.5\% & 74.50\% & 72.56\% & **76.34\%** \\
4 & Wine & 3-Class & 62.17\% & 64.7\% & 65.19\% & 66.20\% & 66.59\% & 68.25\% & **70.51\%** \\
5 & Seed & 3-Class & 70.56\% & 75.16\% & 75.29\% & 75.77\% & 82.13\% & 86.76\% & **91.54\%** \\
6 & Vertebral & 2-Class & 84.95\% & 87.13\% & 89.26\% & 91.15\% & 93.64\% & 90.17\% & **96.25\%** \\
7 & Blood Tissue & 2-Class & 73.47\% & 76.23\% & 74.16\% & 72.21\% & 84.76\% & 81.34\% & **86.45\%** \\
9 & Memo Graphy & 2-Class & 71.26\% & 74.39\% & 68.37\% & 82.45\% & 86.17\% & 96.66\% & **99.21\%** \\ \hline \hline \end{tabular}
\end{table}