# Proposing Enhanced Feature Engineering and a Selection Model for Machine Learning Processes
## 3 Groundwork of eFES
### eFES Algorithms
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**DS** & **RF** & **LASSO** & **RR** & **EN** & **GBM** & **LDA** & **MDA** & **JMI** & **NNMF** & **ePES** & **Winner** \\ \hline
11 & 0.733 & 0.763 & 0.824 & 0.473 & 0.610 & 0.786 & 0.522 & 0.731 & 0.862 & 0.893 & **ePES** \\
12 & 0.838 & 0.864 & 0.549 & 0.772 & 0.584 & 0.910 & 0.760 & 0.706 & 0.631 & 0.845 & LDA \\
13 & 0.611 & 0.964 & 0.928 & 0.781 & 0.565 & 0.703 & 0.550 & 0.827 & 0.908 & 0.923 & LASSO \\
14 & 0.905 & 0.923 & 0.754 & 0.807 & 0.643 & 0.670 & 0.605 & 0.531 & 0.650 & 0.982 & **ePES** \\
15 & 0.640 & 0.601 & 0.820 & 0.950 & 0.512 & 0.948 & 0.827 & 0.786 & 0.662 & 0.734 & EN \\
16 & 0.530 & 0.690 & 0.621 & 0.622 & 0.808 & 0.934 & 0.630 & 0.537 & 0.931 & 0.956 & **ePES** \\
17 & 0.547 & 0.825 & 0.512 & 0.711 & 0.740 & 0.877 & 0.766 & 0.697 & 0.561 & 0.893 & **ePES** \\
18 & 0.632 & 0.642 & 0.891 & 0.670 & 0.864 & 0.665 & 0.774 & 0.902 & 0.702 & 0.962 & **ePES** \\
19 & 0.728 & 0.671 & 0.720 & 0.726 & 0.743 & 0.582 & 0.550 & 0.781 & 0.631 & 0.704 & JMI \\
20 & 0.836 & 0.746 & 0.574 & 0.585 & 0.979 & 0.872 & 0.758 & 0.941 & 0.952 & 0.942 & GBM \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Performance Evaluation (PE) = Precision Score. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**DS** & **RF** & **LASSO** & **RR** & **EN** & **GBM** & **LDA** & **MDA** & **JMI** & **NNMF** & **eFES** & **Winner** \\ \hline
11 & 0.569 & 0.808 & 0.739 & 0.633 & 0.848 & 0.563 & 0.518 & 0.540 & 0.874 & 0.810 & PCA \\
2 & 0.513 & 0.796 & 0.800 & 0.643 & 0.610 & 0.659 & 0.618 & 0.664 & 0.589 & 0.762 & CS \\
3 & 0.784 & 0.636 & 0.781 & 0.589 & 0.499 & 0.585 & 0.539 & 0.858 & 0.717 & 0.96 & **eFES** \\
4 & 0.592 & 0.834 & 0.498 & 0.788 & 0.789 & 0.713 & 0.911 & 0.830 & 0.645 & 0.976 & **eFES** \\
5 & 0.655 & 0.698 & 0.805 & 0.504 & 0.880 & 0.574 & 0.638 & 0.885 & 0.742 & 0.699 & WOE \\
6 & 0.590 & 0.741 & 0.791 & 0.825 & 0.654 & 0.826 & 0.698 & 0.679 & 0.962 & 0.892 & PCA \\
7 & 0.802 & 0.626 & 0.680 & 0.510 & 0.896 & 0.745 & 0.646 & 0.735 & 0.974 & 0.740 & PCA \\
8 & 0.805 & 0.560 & 0.550 & 0.826 & 0.609 & 0.812 & 0.659 & 0.704 & 0.814 & 0.894 & **eFES** \\
9 & 0.642 & 0.802 & 0.769 & 0.891 & 0.504 & 0.482 & 0.629 & 0.830 & 0.734 & 0.836 & ANOVA \\
10 & 0.872 & 0.898 & 0.858 & 0.785 & 0.921 & 0.573 & 0.831 & 0.754 & 0.868 & 0.971 & **eFES** \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Performance Evaluation (PE) = AUC, ideally higher values are desired. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**DS** & **RF** & **LASSO** & **RR** & **EN** & **GBM** & **LDA** & **MDA** & **JMI** & **NNMF** & **eFES** & **Winner** \\ \hline
11 & 0.569 & 0.808 & 0.739 & 0.633 & 0.848 & 0.563 & 0.518 & 0.540 & 0.874 & 0.810 & PCA \\
2 & 0.513 & 0.796 & 0.800 & 0.643 & 0.610 & 0.659 & 0.618 & 0.664 & 0.589 & 0.762 & CS \\
3 & 0.784 & 0.636 & 0.781 & 0.589 & 0.499 & 0.585 & 0.539 & 0.858 & 0.717 & 0.96 & **eFES** \\
4 & 0.592 & 0.834 & 0.498 & 0.788 & 0.789 & 0.713 & 0.911 & 0.830 & 0.645 & 0.976 & **eFES** \\
5 & 0.655 & 0.698 & 0.805 & 0.504 & 0.880 & 0.574 & 0.638 & 0.885 & 0.742 & 0.699 & WOE \\
6 & 0.590 & 0.741 & 0.791 & 0.825 & 0.654 & 0.826 & 0.698 & 0.679 & 0.962 & 0.892 & PCA \\
7 & 0.802 & 0.626 & 0.680 & 0.510 & 0.896 & 0.745 & 0.646 & 0.735 & 0.974 & 0.740 & PCA \\
8 & 0.805 & 0.560 & 0.550 & 0.826 & 0.609 & 0.812 & 0.659 & 0.704 & 0.814 & 0.894 & **eFES** \\
9 & 0.642 & 0.802 & 0.769 & 0.891 & 0.504 & 0.482 & 0.629 & 0.830 & 0.734 & 0.836 & ANOVA \\
10 & 0.872 & 0.898 & 0.858 & 0.785 & 0.921 & 0.573 & 0.831 & 0.754 & 0.868 & 0.971 & **eFES** \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Performance Evaluation (PE) = AUC.