# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 4 Computational Study
These metrics are then saved while being associated with the feature selection method and dataset involved in the current step. ```
Data:\(D_{info}=<path,cf,l>\) Input:\(D_{info},ffs_{current},tl\) Output:\(PerformanceMetrics\) Algorithm \(D\gets ReadDataset(D_{info})\) if\(Rows(D)>10000\)then \(D\gets StratifiedSelect(D,10000)\) if\(D_{info}[cf]\) not emptythen \(D[cf]\gets CategoricalEncoder(D[cf],D[l])\) \(D^{\prime}\gets ffs_{current}(D,TargetLength=tl)\) \(R_{classifier}\gets TrainRandomForest(D^{\prime})\) \(PerformanceMetrics\gets GetMetrics(RF_{classifier})\) return\(PerformanceMetrics\)
```  
**Algorithm 2**Testing cycle's algorithm \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **Source** & **Size** & **Features** & **Classification Type** & **Label Values** \\ \hline API security: Access behavior anomaly dataset & [69] & 1699 & 9 & Binary & 2 \\ \hline Banking Dataset Classification & [70] & 32,950 & 20 & Binary & 2 \\ \hline Bank Marketing & [71] & 45,211 & 16 & Binary & 2 \\ \hline Breast Cancer Wisconsin (Original) & [72] & 699 & 9 & Binary & 2 \\ \hline Churn Modeling & [73] & 10,000 & 13 & Binary & 2 \\ \hline Taiwanese Bankruptcy Prediction & [74] & 6819 & 95 & Binary & 2 \\ \hline CSGO Round Winner Classification & [75] & 122,410 & 96 & Binary & 2 \\ \hline Cure The Princess & [76] & 2338 & 13 & Binary & 2 \\ \hline Depression & [77] & 1429 & 22 & Binary & 2 \\ \hline EEG Eye State & [78] & 14,980 & 14 & Binary & 2 \\ \hline Statlog (German Credit Data) & [79] & 1000 & 20 & Binary & 2 \\ \hline GlaucomaM Dataset & [80] & 196 & 62 & Binary & 2 \\ \hline Heart Failure Prediction Dataset & [81] & 918 & 11 & Binary & 2 \\ \hline Home Loan Approval & [82] & 614 & 11 & Binary & 2 \\ \hline IBM Artrition Dataset & [83] & 1470 & 12 & Binary & 2 \\ \hline Ionosphere & [84] & 351 & 34 & Binary & 2 \\ \hline MAGIC Gamma Telescope & [85] & 19,020 & 10 & Binary & 2 \\ \hline Microcalcification Classification & [86] & 11,183 & 6 & Binary & 2 \\ \hline Musk (Version 1) & [87] & 476 & 168 & Binary & 2 \\ \hline Parkinsons & [88] & 197 & 22 & Binary & 2 \\ \hline Pima Indians Diabetes Database & [89] & 768 & 8 & Binary & 2 \\ \hline HTRU2 & [90] & 17,898 & 8 & Binary & 2 \\ \hline Rice Type Classification & [91] & 18,185 & 10 & Binary & 2 \\ \hline Smoke Detection Dataset & [92] & 62,630 & 12 & Binary & 2 \\ \hline Connectionist Bench (Sonar, Mines vs. Rocks) & [93] & 208 & 60 & Binary & 2 \\ \hline Spambase & [94] & 4601 & 57 & Binary & 2 \\ \hline Spotify Song Attributes & [95] & 2017 & 16 & Binary & 2 \\ \hline Telescope Spectrum Gamma or Hadron & [96] & 19,020 & 10 & Binary & 2 \\ \hline Titanic Dataset & [97] & 891 & 10 & Binary & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of datasets used in the computational study. For this test, PCA was the chosen extraction method. This was the case since PCA has been extensively utilized in various experimental settings, particularly in the fields of biometrics, image processing, and fault detection, due to its effectiveness in feature extraction. Some notable examples are its usage in the realm of iris and face recognition [129; 130; 131]. However, one limitation of PCA is that it may fail to capture complex non-linear structures that are often present in real-world datasets. To address this limitation, the approach chosen was to use Random Fourier Features (RFF). RFF approximates non-linear kernel methods (specifically those based on shift-invariant kernels like the Radial Basis Function) by mapping the input data into a randomized low-dimensional feature space where linear techniques can then be applied [132]. Additionally, Mutual Information (MI) was employed as the filter-based feature selection algorithm for this experiment. This choice was motivated by several factors. First, MI has consistently demonstrated effectiveness across a wide range of feature selection scenarios, making it a robust and well-validated method [133; 134]. Second, MI is computationally