# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 4 Computational Study
While not formally benchmarked, this suggests that pre-processing does not materially affect the relative performance comparisons reported. In this study, all feature selection methods and the downstream random forest classifier were evaluated using their default hyper-parameters. This decision was guided by considerations of fairness, computational feasibility, and reproducibility. Using default settings ensured a consistent baseline across all methods, allowing performance differences to reflect algorithmic behavior rather than hyperparameter tuning. Moreover, auto-tuning would have introduced a prohibitive computational overhead. Given the scale of the study--ten optimizers (the proposed approach plus nine wrapper approaches), sixty datasets, and multiple feature set sizes--exploring additional hyperparameter combinations would have multiplied the runtime significantly, rendering the analysis impractical. Finally, default configurations reflect typical usage scenarios, making the results more accessible and easier to replicate. The chosen defaults are generally sensible values that offer a good balance between performance and abstraction of usage. While future work may explore optimized configurations, the use of defaults provided a balanced and efficient framework for large-scale comparison. Due to the design of the wrapper-based feature selection methods (where the best feature subsets are selected independently of the target subset length), it is possible that fewer than thirty valid data points were obtained for certain target lengths \(r\). While this does not pose a methodological issue, it limits the extent to which these subsets can be assumed to follow a normal distribution under the central limit theorem [136]. To address this, all wrapper-based methods were later aggregated into a single group during the statistical analysis, thereby ensuring a sufficient sample size and enabling a more reliable approximation to normality for comparative purposes. The metrics used to measure the performance of the fitted classifiers were:  
* Weighted Precision--obtained by calculating the precision for each involved label, and finding their average weighted by the number of true instances for each label. * Weighted Recall--obtained by calculating the recall for each involved label, and finding their average weighted by the number of true instances for each label. * Weighted Fbeta--obtained by calculating the fbeta score for each involved label, and finding their average weighted by the number of true instances for each label. * Time--obtained by measuring the elapsed time during the testing process The collected results were used to plot the graphs seen in Figures 5-12. To further explore the collected results, and statistically test them, all data points were then divided into two groups \"Proposed\" and \"Other\", which took into account the proposed method (PM) data points and all other data points, respectively. Thus, the central limit theorem was respected in both groups, meaning both should converge to a standard normal distribution. By plotting these new data groups according to the metrics mentioned earlier, Figures 13-20 are produced. Specifically, in the case of the points relating to the elapsed time, the data regarding MA and RDA were excluded so as to not negatively impact the other approaches. To statistically validate the observed performance differences between the proposed method and the baseline approaches, we applied a combination of [PERSON]'s two-tailed \(t\)-tests and Generalized Least Squares (GLS) regression models. [PERSON]'s \(t\)-test is appropriate for comparing two groups with potentially unequal variances and sample sizes, which suits our experimental setting where the number of valid data points varies across methods and feature set sizes. GLS modeling further allowed us to estimate the effect of the independent variables (specifically, the feature set length and method group) on each performance metric while controlling for task type (binary or multi-class classification).