# 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} \hline \hline
**DS** & **RF** & **LASSO** & **RR** & **EN** & **GBM** & **LDA** & **MDA** & **JMI** & **NNMF** & **eFES** & **Winner** \\ \hline
11 & 0.666 & 0.816 & 0.913 & 0.621 & 0.206 & 0.656 & 0.930 & 0.978 & 0.586 & 0.912 & US \\
2 & 0.762 & 0.754 & 0.502 & 0.926 & 0.959 & 0.774 & 0.915 & 0.566 & 0.875 & 0.925 & WOE \\
3 & 0.921 & 0.207 & 0.691 & 0.757 & 0.920 & 0.520 & 0.846 & 0.932 & 0.758 & 0.623 & US \\
4 & 0.693 & 0.542 & 0.673 & 0.500 & 0.765 & 0.924 & 0.647 & 0.501 & 0.824 & 0.957 & **eFES** \\
5 & 0.773 & 0.533 & 0.775 & 0.615 & 0.814 & 0.535 & 0.682 & 0.536 & 0.878 & 0.856 & PCA \\
6 & 0.685 & 0.910 & 0.568 & 0.606 & 0.698 & 0.831 & 0.646 & 0.902 & 0.851 & 0.945 & **eFES** \\
7 & 0.635 & 0.716 & 0.676 & 0.793 & 0.593 & 0.802 & 0.843 & 0.671 & 0.930 & 0.991 & **eFES** \\
8 & 0.667 & 0.877 & 0.918 & 0.751 & 0.854 & 0.930 & 0.794 & 0.527 & 0.936 & 0.875 & PCA \\
9 & 0.897 & 0.644 & 0.545 & 0.517 & 0.762 & 0.802 & 0.685 & 0.865 & 0.650 & 0.834 & IG \\
10 & 0.599 & 0.824 & 0.803 & 0.802 & 0.827 & 0.875 & 0.933 & 0.851 & 0.724 & 0.925 & **eFES** \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Performance Evaluation (PE) = Cohen’s Kappa, ideally higher values are desired. \begin{table}
\begin{tabular}{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 & 0.892 & 0.929 & 0.819 & 0.874 & 0.537 & 0.662 & 0.833 & 0.581 & 0.857 & 0.983 & **eFES** \\
12 & 0.954 & 0.660 & 0.944 & 0.489 & 0.582 & 0.869 & 0.753 & 0.786 & 0.771 & 0.973 & **eFES** \\
13 & 0.576 & 0.952 & 0.686 & 0.588 & 0.744 & 0.712 & 0.658 & 0.927 & 0.671 & 0.910 & LASSO \\
14 & 0.519 & 0.780 & 0.505 & 0.850 & 0.603 & 0.731 & 0.94

## 6 Final Remarks

### Conclusions

This paper reports the latest progress of the proposed model for enhanced Feature Engineering and Selection (eFES) including mathematical constructs, framework, and the algorithms. eFES is a module of enhanced Machine Learning Engine Engineering (eMLEE) parent model. eFES is based on the following building blocks: (a) a features set is processed through standard methods and records the measured metrics; (b) features are weighted based on learning process where accepted features and rejected features are separated using 3D-based training through building Local Gain (LG) and Global Gain (GG) functions; (c) features are then scored and optimized so the ML process can evolve into deciding which features need to be accepted or rejected for improved generalization of the model; (d) finally features are evaluated, tested, and the model is completed with feature grouping function (FGF). This paper reports observation on several hundreds of experiments and then implements 10 experimental approaches to tune the model. The 10 th experimental rule was adopted to narrow down (i.e., slice) the result extraction from several hundred runs. The LG and GG functions were built and optimized in 3D space. The included results show promising outcomes of the proposed scheme of the eFES model. It supports the use of feature sets to further optimize the learning process of ML models for supervised learning. Using the novel approach of Local Error and Global Error bounds of 20% to 80%, we could tune our model more realistically. If the errors were above 80% or below 20%, we flag it to be an invalid fit. This unique approach of engineering a model turns out to be very effective in our experiments and observations, as reported and discussed in this paper. This model though is based on parallel processing but using high-speed hardware or a Hadoop-based system will help further. Features (i.e., attributes) in the datasets are often irrelevant and redundant and may have less predictive value. Therefore, we constructed these two functions. _A_) Irrelevant _Irr.F_ (Equation (8)) and _B_) _Red.F_ (Algorithm 1). The real-world data may have more features and based on this exact fact, we realized the gap to fill with our work.