# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 4 Computational Study
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Dataset** & **Source** & **Size** & **Features** & **Classification Type** & **Label Values** \\ \hline Water Quality & [98] & 7999 & 20 & Binary & 2 \\ \hline Personality classification Data: 16 Personalities & [99] & 60,000 & 60 & Multiclass & 16 \\ \hline Customer Segmentation Classification & [100] & 2627 & 9 & Multiclass & 4 \\ \hline Body Performance Data & [101] & 13,393 & 11 & Multiclass & 4 \\ \hline COVID-19 Dataset & [102] & 1,048,576 & 20 & Multiclass & 7 \\ \hline Dermatology & [103] & 366 & 34 & Multiclass & 6 \\ \hline DnD Stats & [104] & 10,000 & 9 & Multiclass & 9 \\ \hline Microsoft Malware Sample & [105] & 1642 & 257 & Multiclass & 9 \\ \hline Glass Identification & [106] & 214 & 9 & Multiclass & 7 \\ \hline Weight Lifting Exercise Dataset & [107] & 19,622 & 152 & Multiclass & 5 \\ \hline HCV Data & [108] & 615 & 12 & Multiclass & 5 \\ \hline Relationship Between Pain and MyersBriggs Personality Factors & [109] & 97 & 18 & Multiclass & 4 \\ \hline League of Legends Stats: S12 & [110] & 232 & 9 & Multiclass & 6 \\ \hline Letter Recognition & [111] & 20,000 & 16 & Multiclass & 26 \\ \hline Crystal System Properties for Li-ion Batteries & [112] & 339 & 10 & Multiclass & 3 \\ \hline Clean Microsoft Malware Classification Challenge Dataset (BIG 2015) & [113] & 10,868 & 68 & Multiclass & 9 \\ \hline Milk Quality Prediction & [114] & 1059 & 7 & Multiclass & 3 \\ \hline Prediction of Music Genre & [115] & 50,005 & 17 & Multiclass & 10 \\ \hline Orbit Classification for Prediction NASA & [116] & 1748 & 11 & Multiclass & 6 \\ \hline Pokemon Unite Dataset & [117] & 53 & 9 & Multiclass & 5 \\ \hline Poker Hand & [118] & 1,025,010 & 10 & Multiclass & 10 \\ \hline Pump Sensor Dataset & [119] & 220,320 & 52 & Multiclass & 3 \\ \hline Seeds & [120] & 210 & 7 & Multiclass & 3 \\ \hline Sleep Health and Lifestyle Dataset & [121] & 374 & 12 & Multiclass & 3 \\ \hline Soybean (Large) & [122] & 307 & 35 & Multiclass & 19 \\ \hline Star Types Dataset & [123] & 240 & 6 & Multiclass & 6 \\ \hline Vehicles Silhouettes & [124] & 846 & 18 & Multiclass & 4 \\ \hline Video Games Rating by ESRB\({}^{\prime}\) & [125] & 1895 & 33 & Multiclass & 4 \\ \hline Vowel Recognition-Deterding Data & [126] & 990 & 10 & Multiclass & 11 \\ \hline Wine Quality & [127] & 4898 & 11 & Multiclass & 11 \\ \hline Zoo & [128] & 101 & 16 & Multiclass & 7 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._efficient, which aligns well with the broader objective of this study to maintain reasonable resource usage across large-scale evaluations. Finally, unlike correlation metrics, such as Pearson's or Kendall's coefficient, MI can capture both non-linear and non-monotonic dependencies between features and the target variable [135]. This makes it particularly suitable for complex datasets where relationships among variables may not be simplistic. Prior to applying any feature selection methods, all datasets used for testing underwent a standardized pre-processing pipeline. Rows with missing values were removed to ensure consistent input for downstream processing (although these were few in number due to this being a factor during dataset selection). Numerical features were standardized (using Z-score scaling) to ensure comparability across dimensions and to prevent features with larger magnitudes from dominating others. Categorical features were encoded within the evaluation loop itself, using target encoding with smoothing to avoid introducing target leakage. This design was adopted in order to allow for easy substitution of encoding strategies if needed. Notably, one-hot encoding was avoided, as it would have prohibitively inflated the dimensionality of the feature space. It is important to note that the pre-processing step was applied uniformly across all methods. Based on empirical observations during testing, the time spent on pre-processing was consistently negligible.