# Constructing Features Using a Hybrid Genetic Algorithm
## 3 Experiments
### Experimental Results  
Table 4 represents the comparative results for the classification datasets and Table 5 shows the results for the regression problems. For the case of classification problems, the average classification error is reported, while for the regression problem, the average per \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Features** & **Patterns** \\ \hline Balance & 4 & 625 \\ \hline BK & 4 & 96 \\ \hline BL & 7 & 41 \\ \hline Dermatology & 34 & 359 \\ \hline Glass & 9 & 214 \\ \hline Hayes Roth & 5 & 132 \\ \hline Heart & 13 & 270 \\ \hline Housing & 13 & 506 \\ \hline Ionosphere & 34 & 351 \\ \hline Laser & 4 & 993 \\ \hline NT & 2 & 131 \\ \hline Parkinson’s & 22 & 195 \\ \hline Pima & 8 & 768 \\ \hline PopFailures & 18 & 540 \\ \hline PY & 27 & 74 \\ \hline Quake & 3 & 2178 \\ \hline FA & 18 & 252 \\ \hline Spatial & 2 & 2000 \\ \hline Wine & 13 & 179 \\ \hline Wdbc & 30 & 569 \\ \hline Z\_F\_S & 21 & 300 \\ \hline Z\_O\_N\_F\_S & 21 & 500 \\ \hline ZO\_NF\_S & 21 & 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Features and patterns for every experimental dataset. point error is reported. The proposed method is denoted as FC MLP and it is compared against four other approaches from the relevant literature:  
1. The minimum redundancy maximum relevance feature selection method [78,79] with two selected features. This approach is denoted as MRMR in the experimental tables. The features selected by MRMR are evaluated using an artificial neural network trained by a genetic algorithm with \(N_{c}\) chromosomes. 2. The principal component analysis (PCA) method as implemented in the Mlpack software [80]. The PCA method is used to construct two features from the original dataset. Subsequently, these features are evaluated using an artificial neural network trained by a genetic algorithm with \(N_{c}\) chromosomes. 3. A genetic algorithm with \(N_{c}\) chromosomes and the parameters of Table 2 used to train a neural network with \(H\) hidden nodes. This approach is denoted as MLP GEN in the experimental tables. 4. A particle swarm optimization (PSO) with \(N_{c}\) particles and a \(N_{g}\) number of generations used to train a neural network with \(H\) hidden nodes. This method is denoted as MLP PSO in the experimental tables. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **MRMR** & **PCA** & **MLP GEN** & **MLP PSO** & **FC MLP** \\ \hline Balance & 56.80\% & 56.48\% & 8.23\% & 8.07\% & 0.30\% \\ \hline Dermatology & 68.54\% & 62.11\% & 10.01\% & 17.57\% & 4.98\% \\ \hline Glass & 58.35\% & 50.16\% & 58.03\% & 57.35\% & 45.84\% \\ \hline Hayes Roth & 61.21\% & 61.13\% & 35.26\% & 36.69\% & 23.26\% \\ \hline Heart & 38.04\% & 35.84\% & 25.46\% & 25.67\% & 17.71\% \\ \hline Ionosphere & 12.93\% & 21.22\% & 13.67\% & 15.14\% & 8.42\% \\ \hline Parkinson’s & 17.16\% & 16.96\% & 17.47\% & 18.35\% & 10.10\% \\ \hline Pima & 26.29\% & 39.43\% & 32.98\% & 30.45\% & 23.76\% \\ \hline PopFailures & 7.04\% & 31.42\% & 7.66\% & 6.24\% & 4.66\% \\ \hline Spiral & 44.87\% & 45.94\% & 45.71\% & 42.10\% & 26.53\% \\ \hline Wine & 30.73\% & 30.39\% & 20.82\% & 19.31\% & 7.31\% \\ \hline Wdbc & 12.91\% & 10.28\% & 6.32\% & 6.95\% & 3.47\% \\ \hline Z\_F\_S & 32.71\% & 44.81\% & 9.42\% & 10.38\% & 5.52\% \\ \hline Z\_O\_N\_F\_S & 43.04\% & 56.45\% & 60.38\% & 63.56\% & 31.20\% \\ \hline ZO\_NF\_S & 33.79\% & 40.02\% & 8.06\% & 8.84\% & 4.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results for classification datasets.