# An Improved Particle Swarm Optimization Algorithm for Data Classification
## 3 Results and Discussion
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Sr. No** & **Dataset** & **No of Attributes** & **Number of Labels** & **Number of Records** \\ \hline
1 & Iris & 4 & 3 & 150 \\
2 & Wheat seed & 7 & 3 & 210 \\
3 & Pima India & 8 & 2 & 768 \\  & Diabetes & & & \\
4 & Heart & 13 & 2 & 270 \\  & Disease & & & \\  & Wisconsin & & & \\
5 & Breast & 10 & 2 & 699 \\  & Cancer & & & \\
6 & Vertebral & 6 & 2 & 310 \\
7 & Wine & 13 & 3 & 178 \\  & Haberman's & 3 & 2 & 306 \\
8 & survival & & & \\
9 & Balance scale & 4 & 3 & 625 \\  & Blood & & & \\
10 & Transfusion & 4 & 2 & 748 \\
11 & Sonar & 60 & 2 & 208 \\  & Bank Note & & & \\
12 & Authentication & 4 & 2 & 1372 \\  & tion & & & \\
13 & Ionosphere & 34 & 2 & 351 \\
14 & Liver & 6 & 2 & 345 \\  & Disorder & & & \\
15 & Car & & & \\  & Evaluation & 6 & 4 & 1728 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Datasets detail. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Relation** & **Sum of Squares** & **df** & **Mean Square** & **F** & **Significance** \\ \hline Testing Accuracy & Between groups & 903.2158 & 5 & 180.6432 & 2.334319 & 0.049042 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: One-way ANOVA result test of QRS with PSO approaches. A one-way ANOVA test with a significance level of 0.05 was implemented on the testing accuracy of six PSONN approaches. Table 3 depicts the results of the one-way ANOVA test. The significance value in Table 3 is 0.0494, which is smaller than 0.05, showing that there is a significant difference between all PSONN variants with a 95% confidence level. Therefore, PSONN variants are significantly different from each other. Figure 12 depicts the one-way ANOVA test results, which shows OPSO-F significantly has better results than other PSO approaches. The graph of ANOVA test show the OPSO-F is significantly distinct from all other PSO approaches. The weights of the feed-forward neural network were trained using opposition-based PSO ranked inertia weight (ORIW-PSO), [PERSON] with opposition-based PSO ranked inertia weight (ORIW-PSO-S), [PERSON] with opposition-based PSO ranked inertia weight (ORIW-PSO-H), log normal with opposition-based PSO ranked inertia weight (ORIW-PSO-LN), [PERSON] with opposition-based PSO ranked inertia weight (ORIW-PSO-G), and [PERSON] with opposition-based PSO ranked inertia weight (ORIW-PSO-F). We prepared a feed-forward neural network using the weight optimisation process. The performance of ORIW-PSO, ORIW-PSO-S, ORIW-PSO-H, ORIW-PSO-G, ORIW-PSO-LN, ORIW-PSO-F and state-of-the-art NN algorithms have been tested on 15 well-known datasets. To evaluate the performance of the proposed ORIW-PSO-F technique, it was compared with variants of PSO such as ORIW-PSO, ORIW-PSO-S, ORIW-PSO-H, ORIW-PSO-G, and ORIW-PSO-LN on fifteen real data sets extracted from UCI. The detail of these fifteen data sets is presented in Figure 13, where ORIW-PSO-F is well-performing on these datasets and shows good results. Simulation results in Figure 13 show that neural network training using the ORIW-PSO-F algorithm performed well and provided better accuracy than other PSO approaches. After simulation, the result is excellent in training the NN using the ORIW-PSO-F algorithm, and shows better precision and accuracy than traditional approaches. The ORIW-PSO-F algorithm can be effectively used for real-world complex statistical problems and data classification problems in the future. The accuracy results of classification problems are depicted in Figure 14, and the accuracy graph is represented in the same figure for fifteen data sets. Figure 10: Result of one-way ANOVA test on testing accuracy (QRS with PSO approaches). Figure 11: Testing accuracy result of QRS with opposition-based PSO approaches. Figure 12: One-way ANOVA test on testing accuracy. The one-way ANOVA test with a significance level of 0.05 was implemented on testing accuracy of six PSONN approaches. Table 4 shows the results of the one-way ANOVA test.