## 3 Experimental Results
### Experimental Results
#### 3.3.1 Experiments with the Parameter \(N_{l}\)  
To verify the robustness of the proposed technique as well as its sensitivity to parameter changes, a new experiment was carried out in which the critical \(N_{l}\) parameter was varied from 5 to 20. This parameter determines the number of generations intervening before the local optimization method is executed on randomly selected chromosomes. The results from this experiment for the classification datasets are depicted in Table 6, and the results for the regression datasets are depicted in Table 7. \begin{table}
\begin{tabular}{c c c c} \hline
**DATASET** & **INNC (\(N_{l}=5\))** & **INNC (\(N_{l}=10\))** & **INNC (\(N_{l}=20\))** \\ \hline APPENDICITIS & 14.50\% & 14.70\% & **14.20\%** \\ \hline AUSTRALIAN & **14.48\%** & 14.80\% & 14.58\% \\ \hline BALANCE & **7.63\%** & 8.66\% & 11.71\% \\ \hline CIRCULAR & **5.25\%** & 5.32\% & 5.87\% \\ \hline CLEVELAND & 48.41\% & **47.93\%** & 49.66\% \\ \hline DERMATOLOGY & **19.66\%** & 20.89\% & 23.11\% \\ \hline ECOLI & **47.39\%** & 48.21\% & 48.09\% \\ \hline FERT & 20.80\% & **20.50\%** & 20.90\% \\ \hline HABERMAN & 26.87\% & **26.70\%** & 27.27\% \\ \hline HAYES-ROTH & **31.69\%** & 31.77\% & 35.92\% \\ \hline HEART & 14.85\% & **14.74\%** & 15.56\% \\ \hline HEARTATTACK & 20.10\% & 20.43\% & **20.07\%** \\ \hline HOUSEVOTES & 3.87\% & **3.26\%** & 3.78\% \\ \hline \end{tabular}
\end{table} 
Table 6: Experimental results using a variety of values for the parameter \(N_{l}\) of the current work with application on the classification datasets. The bold notation marks the method with the lowest test error. Figure 7: Statistical test for all machine learning models that was applied on the regression datasets. \begin{table}
\begin{tabular}{c c c c} \hline
**DATASET** & **INNC (\(N_{I}=5\))** & **INNC (\(N_{I}=10\))** & **INNC (\(N_{I}=20\))** \\ \hline IONOSPHERE & 11.51\% & 11.92\% & **11.09\%** \\ \hline LIVERDISORDER & **31.35\%** & 31.77\% & 31.85\% \\ \hline MAMMOGRAPHIC & 16.25\% & **15.81\%** & 16.06\% \\ \hline PARKINSONS & 13.16\% & **12.53\%** & 12.58\% \\ \hline PIMA & **23.95\%** & 24.00\% & 24.67\% \\ \hline POPFAILURES & **6.06\%** & 6.44\% & 6.48\% \\ \hline REGIONS2 & 23.36\% & **23.18\%** & 24.21\% \\ \hline SAHEART & **27.68\%** & 28.09\% & 29.41\% \\ \hline SEGMENT & 43.79\% & **43.12\%** & 46.91\% \\ \hline SPIRAL & **43.63\%** & 43.99\% & 45.15\% \\ \hline STUDENT & **3.65\%** & 4.55\% & 3.88\% \\ \hline TRANSFUSION & **22.62\%** & 23.43\% & 23.89\% \\ \hline WDBC & **4.41\%** & 4.41\% & 5.46\% \\ \hline WINE & 10.18\% & **9.77\%** & 12.00\% \\ \hline Z\_F\_S & **7.70\%** & 8.53\% & 9.63\% \\ \hline Z\_O\_N\_F\_S & **36.64\%** & 38.58\% & 41.08\% \\ \hline ZO\_NF\_S & **6.84\%** & 6.84\% & 6.90\% \\ \hline ZONF\_S & **2.24\%** & 2.52\% & 2.66\% \\ \hline ZOO & **6.30\%** & 7.20\% & 7.70\% \\ \hline
**AVERAGE** & **19.28\%** & **19.52\%** & **20.39\%** \\ \hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline
**DATASET** & **INNC (\(N_{I}=5\))** & **INNC (\(N_{I}=10\))** & **INNC (\(N_{I}=20\))** \\ \hline ABALONE & 4.38 & **4.33** & 4.45 \\ \hline AIRFOIL & 0.002 & **0.002** & 0.002 \\ \hline BASEBALL & **47.50** & 48.42 & 49.99 \\ \hline BK & 0.64 & **0.07** & 1.86 \\ \hline BL & 0.014 & **0.002** & 0.003 \\ \hline CONCRETE & **0.005** & 0.005 & 0.005 \\ \hline DEE & **0.22** & 0.23 & 0.23 \\ \hline HO & **0.01** & 0.01 & 0.011 \\ \hline HOUSING & 21.09 & **16.01** & 16.96 \\ \hline FY & 0.046 & **0.042** & 0.19 \\ \hline LASER & **0.004** & 0.005 & 0.005 \\ \hline LW & **0.011** & 0.012 & 0.011 \\ \hline MORTGAGE & **0.02** & 0.026 & 0.03 \\ \hline MUNDIAL & 0.031 & 0.034 & **0.029** \\ \hline PL & **0.022** & 0.022 & 0.022 \\ \hline QUAKE & 0.045 & 0.04 & **0.037** \\ \hline REALESTATE & **68.85** & 70.99 & 70.59 \\ \hline \end{tabular}
\end{table} 
Table 7: Experimental results with a variety of values for the parameter \(N_{I}\). The current work was applied on the regression datasets. The bold notation marks the method with the lowest test error.