Feature Selection for Financial Data Classification Using Random Forest, Boruta, and Recursive Feature Elimination
Bibliographic record
Abstract
The increasing availability of financial data has accelerated the use of machine learning for classification tasks in finance.However, financial datasets are often high-dimensional and noisy, which can degrade model performance and increase computational costs.Feature selection serves as a critical pre-processing step to reduce dimensionality and improve efficiency.This study compares three feature selection methods-Random Forest, Boruta, and Recursive Feature Elimination (RFE)-in the context of financial data classification.The analysis is conducted using three publicly available datasets: Adult Income, Marketing Campaign, and Taiwanese Bankruptcy.A variety of machine learning classifiers are applied to evaluate the impact of feature selection on classification accuracy.Experimental results show that Random Forest Classifier (RFC), particularly with hyperparameter tuning, consistently achieves strong performance across datasets.The combination of RFE and RFC yields the highest accuracy on the Taiwanese Bankruptcy dataset.These findings highlight the importance of selecting relevant features to optimize classification models in finance.The study offers practical insights for enhancing predictive accuracy in financial applications such as credit risk assessment, fraud detection, and customer profiling, thereby contributing to the development of more robust and interpretable machine learning models in the financial sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.015 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".