# Constructing Features Using a Hybrid Genetic Algorithm
## 3 Experiments
### Experimental Datasets  
The method was tested on a series of regression and classification datasets which were mostly obtained from two repositories:  
1. The Machine Learning Repository [[http://www.ics.uci.edu/~mlearn/MLRepository.html](http://www.ics.uci.edu/~mlearn/MLRepository.html)]([http://www.ics.uci.edu/~mlearn/MLRepository.html](http://www.ics.uci.edu/~mlearn/MLRepository.html)) (accessed on 1 April 2022);
2. The Keel Repository [[https://sci2s.ugr.es/keel/](https://sci2s.ugr.es/keel/)]([https://sci2s.ugr.es/keel/](https://sci2s.ugr.es/keel/)) (accessed on 1 April 2022). The description for these datasets is as follows:  
1. **Balance** dataset [62], used in psychological experiments.
2. **Dermatology** dataset [63], which is used for differential diagnosis of erythematosquamous diseases.
3. **Glass** dataset. This dataset contains a glass component analysis for glass pieces that belong to 6 classes.
4. **Hayes Roth** dataset [64].
5. **Heart** dataset [65], used to detect heart disease.
6. **Ionosphere** dataset, a meteorological dataset used in various research papers [66, 67].
7. **Parkinsons** dataset,[68] which is created using a range of biomedical voice measurements from 31 people, among which 23 have Parkinson's disease (PD). The dataset has 22 features.
8. **Pima** dataset, related to diabetes.
9. **PopFailures** dataset [69], used in meteorology.
10. **Spiral** dataset, which is an artificial dataset with two classes. The features in the first class are constructed as: \(x_{1}=0.5t\cos(0.08t),\ x_{2}=0.5t\cos\bigl{(}0.08t+\frac{\pi}{2}\bigr{)}\) and for the second class the used equations are: \(x_{1}=0.5t\cos(0.08t+\pi),\ x_{2}=0.5t\cos\bigl{(}0.08t+\frac{3\pi}{2}\bigr{)}\)
11. **Wine** dataset, which is related to the chemical analysis of wines and it has been used in comparison in various research papers [70, 71].
12. **Wolbc** dataset, which contains data for breast tumors.
13. As a real-world example, consider an EEG dataset described in [72, 73] which is used here. This dataset consists of five sets (denoted as Z, O, N, F and S), each containing 100 single-channel EEG segments which each have 23.6 s duration. With different combinations of these sets, the produced datasets are Z_F_S, ZO_NF_S, ZONF_S.  
The regression datasets are available from the Statlib URL [[http://lib.stat.cmu.edu/datasets/](http://lib.stat.cmu.edu/datasets/)]([http://lib.stat.cmu.edu/datasets/](http://lib.stat.cmu.edu/datasets/)) (accessed on 1 April 2022) and other sources:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(H\) & 10 \\ \hline \(N_{c}\) & 500 \\ \hline \(N_{f}\) & 2 \\ \hline \(p_{s}\) & 0.10 \\ \hline \(p_{m}\) & 0.05 \\ \hline \(N_{g}\) & 200 \\ \hline \(L_{N}\) & \(-\)10.0 \\ \hline \(R_{N}\) & 10.0 \\ \hline \(F\) & 20.0 \\ \hline \(\lambda\) & 100.0 \\ \hline \(\alpha\) & 5.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental parameters.  
**BK** dataset. This dataset comes from smoothing methods in statistics [74] and is used to estimate the points scored per minute in a basketball game. The dataset has 96 patterns of 4 features each.
2. **BL** dataset. This dataset can be downloaded from StatLib. It contains data from an experiment on the effects of machine adjustments on the time to count bolts. It contains 40 patters of 7 features each.
3. **Housing** dataset, described in [75].
4. **Laser** dataset, which is related to laser experiments.
5. **NT** dataset [76], which is related to body temperature measurements.
6. **Quake** dataset, used to estimate the strength of an earthquake.
7. **FA** dataset, which contains a percentage of body fat and ten body circumference measurements. The goal is to fit body fat to the other measurements.
8. **PY** dataset [77], used to learn quantitative structure-activity relationships (QSARs).  
The numbers of features and patterns for every dataset used in the experiments are listed in Table 3.