# Locating the Parameters of RBF Networks Using a Hybrid Particle Swarm Optimization Method
## 3 Experiments
### Experimental Results
This further enhances the robustness and reliability of the proposed technique. Furthermore, in the Table 6, the metrics of precision, recall, and f-score are shown for a series of classification datasets and for the proposed method (IRBF-100) and the classic method for training RBF networks (RBF-KMEANS). In these experimental results, the reader can see the superiority of the proposed technique over the traditional method of training RBF networks. \begin{table}
\begin{tabular}{c c c c} \hline
**DATASET** & \(\mathbf{F=3}\) & \(\mathbf{F=5}\) & \(\mathbf{F=10}\) \\ \hline Appendicitis & 14.43\% & 14.03\% & 14.47\% \\ \hline Australian & 23.45\% & 22.39\% & 23.21\% \\ \hline Balance & 13.35\% & 13.15\% & 11.79\% \\ \hline Bands & 36.48\% & 36.29\% & 36.76\% \\ \hline Cleveland & 49.26\% & 49.64\% & 49.02\% \\ \hline Dermatology & 36.54\% & 35.64\% & 34.37\% \\ \hline Hayes Roth & 39.28\% & 34.13\% & 36.46\% \\ \hline Heart & 15.14\% & 15.60\% & 14.89\% \\ \hline HouseVotes & 4.93\% & 3.90\% & 6.41\% \\ \hline Ionosphere & 7.56\% & 7.52\% & 9.05\% \\ \hline Liverdisorder & 28.37\% & 25.63\% & 28.97\% \\ \hline Lymphography & 20.12\% & 20.02\% & 21.05\% \\ \hline Mammographic & 18.04\% & 17.30\% & 18.21\% \\ \hline Parkinsons & 18.51\% & 13.59\% & 13.49\% \\ \hline Pima & 23.69\% & 23.23\% & 23.52\% \\ \hline Popfailures & 5.76\% & 5.10\% & 4.50\% \\ \hline Regions2 & 25.79\% & 25.77\% & 25.32\% \\ \hline Saheart & 28.89\% & 28.91\% & 26.99\% \\ \hline Segment & 36.53\% & 40.28\% & 43.28\% \\ \hline Spiral & 16.78\% & 19.56\% & 22.18\% \\ \hline Wdbc & 4.64\% & 5.44\% & 5.10\% \\ \hline Wine & 8.31\% & 6.84\% & 8.27\% \\ \hline Z\_F\_S & 4.32\% & 4.18\% & 4.03\% \\ \hline ZO\_NF\_S & 3.70\% & 4.35\% & 3.72\% \\ \hline ZONF\_S & 2.04\% & 2.08\% & 1.98\% \\ \hline ZOO & 11.87\% & 11.13\% & 9.97\% \\ \hline
**AVERAGE** & **18.65\%** & **18.68\%** & **19.12\%** \\ \hline \end{tabular}
\end{table} 
Table 4: Experimental results with the proposed method and using different values for the parameter \(F\) on the classification datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**RBF-KMEANS** & \begin{tabular}{c} **IRBF-100** \\ **DTASET** \\ \end{tabular} & \begin{tabular}{c} **RECISION** \\ \end{tabular} & \begin{tabular}{c} **RECALL** \\ \end{tabular} & \begin{tabular}{c} **F-SCORE** \\ \end{tabular} & \begin{tabular}{c} **PRECISION** \\ \end{tabular} & \
\begin{tabular}{c} **RECALL** \\ \end{tabular} &
\begin{tabular}{c} **F-SCORE** \\ \end{tabular} \\ \hline APPENDICITIS & 0.80 & 0.77 & 0.76 & 0.79 & 0.74 & 0.78 \\ \hline AUSTRALIAN & 0.67 & 0.61 & 0.58 & 0.79 & 0.76 \\ \hline BALANCE & 0.74 & 0.76 & 0.64 & 0.75 & 0.78 & 0.76 \\ \hline BANDS & 0.52 & 0.51 & 0.48 & 0.58 & 0.57 & 0.56 \\ \hline HEART & 0.68 & 0.69 & 0.67 & 0.86 & 0.85 & 0.85 \\ \hline IONOSPHERE & 0.84 & 0.81 & 0.81 & 0.92 & 0.89 & 0.90 \\ \hline LIVERDISORDER & 0.65 & 0.64 & 0.64 & 0.72 & 0.71 & 0.71 \\ \hline MAMMOGRAPHIC & 0.81 & 0.81 & 0.81 & 0.83 & 0.83 & 0.82 \\ \hline PARKINSONS & 0.76 & 0.68 & 0.69 & 0.85 & 0.80 & 0.81 \\ \hline PIMA & 0.72 & 0.67 & 0.68 & 0.75 & 0.70 & 0.71 \\ \hline SAHEART & 0.65 & 0.61 & 0.61 & 0.70 & 0.66 & 0.67 \\ \hline SEGMENT & 0.43 & 0.39 & 0.39 & 0.58 & 0.53 & 0.53 \\ \hline SPIRAL & 0.56 & 0.56 & 0.55 & 0.70 & 0.70 & 0.70 \\ \hline WDBC & 0.93 & 0.91 & 0.92 & 0.96 & 0.94 & 0.95 \\ \hline WINE & 0.74 & 0.65 & 0.66 & 0.93 & 0.93 & 0.92 \\ \hline Z\_F\_S & 0.85 & 0.84 & 0.83 & 0.96 & 0.97 & 0.96 \\ \hline ZO\_NF\_S & 0.90 & 0.90 & 0.90 & 0.95 & 0.95 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Precision, recall, and f-score for a series of classification datasets.