# A Feature Construction Method That Combines Particle Swarm Optimization and Grammatical Evolution
## 3 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline
**DATASET** & **GENETIC** & **RBF** & **ADAM** & **PROP** & **NEAT** \\ \hline ABALONE & 7.17 & 7.37 & 4.30 & 4.55 & 9.88 \\ \hline AIRFOIL} & 0.003 & 0.27 & 0.005 & 0.002 & 0.067 \\ \hline BASEBALL & 103.60 & 93.02 & 77.90 & 92.05 & 100.39 \\ \hline BK & 0.027 & 0.02 & 0.03 & 1.599 & 0.15 \\ \hline BL & 5.74 & 0.01 & 0.28 & 4.38 & 0.05 \\ \hline CONCRETE & 0.0099 & 0.011 & 0.078 & 0.0086 & 0.081 \\ \hline DEE & 1.013 & 0.17 & 0.63 & 0.608 & 1.512 \\ \hline DIABETES & 19.86 & 0.49 & 3.03 & 1.11 & 4.25 \\ \hline HOUSING & 43.26 & 57.68 & 80.20 & 74.38 & 56.49 \\ \hline FA & 1.95 & 0.02 & 0.11 & 0.14 & 0.19 \\ \hline MB & 3.39 & 2.16 & 0.06 & 0.055 & 0.061 \\ \hline MORTGAGE & 2.41 & 1.45 & 9.24 & 9.19 & 14.11 \\ \hline PU & 1.21 & 0.02 & 0.09 & 0.039 & 0.075 \\ \hline QUAKE & 0.04 & 0.071 & 0.06 & 0.041 & 0.298 \\ \hline TREASURY & 2.929 & 2.02 & 11.16 & 10.88 & 15.52 \\ \hline
**AVERAGE** & **12.84** & **10.30** & **11.70** & **12.44** & **12.70** \\ \hline \end{tabular}
\end{table} 
Table 4: Average regression error using the well-known methods for the regression datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{\(f=2\)} & \multicolumn{2}{c}{\(f=3\)} & \multicolumn{2}{c}{\(f=4\)} \\ \hline
**DATASET** & **RBF** & **GENETIC** & **RBF** & **GENETIC** & **RBF** & **GENETIC** \\ \hline APPENDICITIS & 15.40\% & 14.33\% & 16.90\% & 15.77\% & 15.97\% & 17.30\% \\ \hline AUSTRALIAN & 15.49\% & 14.48\% & 14.53\% & 15.33\% & 14.75\% & 15.97\% \\ \hline BALANCE & 16.67\% & 2.89\% & 22.54\% & 4.94\% & 17.26\% & 4.62\% \\ \hline BANDS & 38.09\% & 38.13\% & 37.09\% & 39.22\% & 37.22\% & 35.51\% \\ \hline DERMATOLOGY & 41.58\% & 30.37\% & 35.46\% & 25.44\% & 40.45\% & 21.97\% \\ \hline HAYES ROTH & 37.41\% & 27.92\% & 38.10\% & 25.74\% & 39.59\% & 25.82\% \\ \hline HEART & 21.53\% & 17.13\% & 17.64\% & 16.87\% & 19.63\% & 15.69\% \\ \hline HOUSE VOTES & 6.36\% & 3.78\% & 7.17\% & 3.25\% & 4.25\% & 3.52\% \\ \hline IONOSPIERE & 10.32\% & 10.17\% & 10.12\% & 10.01\% & 11.42\% & 9.02\% \\ \hline LIVERDISORDER & 34.23\% & 32.33\% & 35.84\% & 32.97\% & 35.93\% & 30.74\% \\ \hline LYMOGRAPHY & 34.93\% & 28.67\% & 32.00\% & 23.00\% & 29.00\% & 23.83\% \\ \hline MAMMOGRAPHIC & 16.92\% & 16.51\% & 16.47\% & 16.35\% & 17.54\% & 16.50\% \\ \hline PARKINSONS & 11.14\% & 13.00\% & 11.30\% & 11.11\% & 12.95\% & 9.42\% \\ \hline PIMA & 22.85\% & 22.76\% & 24.93\% & 24.67\% & 24.25\% & 24.20\% \\ \hline POPFAILURES & 7.32\% & 7.41\% & 6.96\% & 7.62\% & 5.96\% & 5.67\% \\ \hline REGIONS2 & 28.52\% & 26.84\% & 24.91\% & 25.28\% & 25.35\% & 24.93\% \\ \hline SAHEART & 29.29\% & 28.63\% & 28.92\% & 30.31\% & 28.25\% & 30.25\% \\ \hline SEGMENT & 52.69\% & 45.59\% & 46.83\% & 41.06\% & 50.15\% & 39.52\% \\ \hline WDBC & 5.00\% & 4.66\% & 5.76\% & 4.97\% & 5.13\% & 3.84\% \\ \hline WINE & 8.92\% & 7.22\% & 6.76\% & 5.75\% & 6.00\% & 5.86\% \\ \hline Z\_F\_S & 7.91\% & 8.37\% & 7.89\% & 7.67\% & 5.21\% & 6.86\% \\ \hline ZOE\_NF\_S & 6.90\% & 6.85\% & 6.95\% & 5.65\% & 6.24\% & 5.28\% \\ \hline ZONE\_S & 3.08\% & 3.40\% & 2.44\% & 2.52\% & 3.47\% & 3.33\% \\ \hline ZOO & 26.47\% & 7.83\% & 31.73\% & 10.03\% & 28.70\% & 11.57\% \\ \hline
**AVERAGE** & **20.79\%** & **17.47\%** & **20.39\%** & **16.90\%** & **20.19\%** & **16.30\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results for the classification datasets using the proposed method. The number in the cells denote the average classification error as measured on the test set. The variable \(f\) corresponds to the number of artificial features created by the proposed method. Figure 3: Average regression error for all regression datasets using the proposed method. The number of PSO particles increased from 100 to 400, and the number of constructed features was set to \(f=2\).