# Proposing Enhanced Feature Engineering and a Selection Model for Machine Learning Processes
## 3 Groundwork of eFES
### eFES Algorithms
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19: Compute: MI for Entropy, CF as correlating factor
20: Re-compute: MI and Red.F (MI)
21: End While
22: End if
23: Define: the categorical or numerical values, and set \(\mathbf{F^{(n)}}=\mathbf{Constant\ value}\)
24: Compute: F.Prep (\(x\), \(y\), \(z\)) \(\leftarrow\) h.blend (ML,z)
25: End for
27: Slice: Data Samples \(\{\Delta\mathbf{S_{n}}\in\mathbf{S}\}\)
28: Compute: and Create Matrices
29: Set: \(\mathbf{G}\left(\mathbf{x},\mathbf{y},\mathbf{z}\right)\leftarrow\frac{\mathbf{1}}{N}\sum_{\mathbf{k }=1}^{\mathbf{1}}(\mathbf{g_{l}})\times\mathbf{MH}\)
30: Entropy (\(\mathbf{F_{n}}\), \(\mathbf{x,y,z}\)) \(\leftarrow\)\(\sum_{\mathbf{i}\mathbf{c}\in\mathbf{c}}\frac{|\mathbf{F_{n}(x,y,z)}|}{|\mathbf{F_{n}}|}\mathbf{Entropy }\left(\mathbf{F_{n}}\right)\)
31: Compute: \(\mathbf{Gain}\left(\mathbf{I},\mathbf{F_{(x,y,z)}}\right)\)/* Using Equation (15) */
32: Compute: \(\mathbf{\text{gR}}\left(\mathbf{z}\right)\) /* Using Equation (16) */
33: End While
34: Reset: x,y,z
35: Update: h.Update (gR (z), h.Prep (x,y,z), CF)
36: Compute: F.Prep (\(x\), \(y\), \(z\)) \(\leftarrow\) h.Model (h*) Return: F.Prep (\(x\), \(y\), \(z\))
```  
**Algorithm 1** Feature Preparation Function--_F.Prep_ (\(x,y,z\))  
Another example was encountered where this feature played significant role in the job industry where a candidate's progress can be impacted based on their religious background. Another example is of GENDER feature/attribute that we discuss in Section 6.1. This also explains our motivation towards creating FGF (Algorithm 2). FGF function determines the right number and type of the features from a given data set during classifier learning and reports accordingly if satisfactory accuracy and generalization have not been reached. eFES unit, as explained in the model earlier, uses 3D array to store the scoring via LT object in the inner layer of the model. Therefore, eFES algorithms can tell the model if more features are needed to finally train the classifier for acceptable prediction in the real-world test. Some other examples are data from healthcare, where a health condition (a feature) may become of high relevance if a certain disease is being predicted. For example, to predict the likelihood of cancer in a patient, DIABETES can have higher predictive score, because an imbalanced sugar can feed the cancerous cells. During learning, the classifier function starts identification of the features and then start adding or removing them based on effectiveness of the cost and time. Compared to other approaches where such proactive quantification is not done the eFES scheme dominates. ``` Input: Sample Dataset (\(\Delta\mathbf{S_{n}}\)), F.Prep (\(x,y,z\)) Output: h.FGF
1: While \(((\mathbf{W}(\mathbf{0}(\mathbf{0})\{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{



Figure 10 simulations demonstrate the global gain and local gain functions coherences with respect to Loss and Cost functions. Figure 10a shows that gain function is unstable when eFES randomly created the feature set. Figure 10b shows that gain functions stabilize when eFES uses weighted function, as derived in the model. ### eFES Framework

Figure 11 illustrates the internal functions of the proposed module on a granular level. eMLEE API refers to the available functions that eMLEE model provides to each module such as eFES. Each grey box is a function. The diamond shapes represent a decision-making point in the flow. ## 4 Results and Discussions

This section provides simulated results in 3D and 2D view to provide in-depth analysis of the outcome of the proposed model for various functions and metrics. Significant samples of the entire experimental results are provided at the latest state of this eFES model development stage. These simulations elaborate on processing features to observe the optimum fitness (i.e., \(z\) dimension). 3D visuals are selected for better analysis of how the curve moves in space when the learner is optimized in the dimensions.