"FRAME: Forward Recursive Adaptive Model Extraction -- A Technique for Advance Feature Selection"

Feature selection is a crucial preprocessing step in machine learning, impacting model performance, interpretability, and computational efficiency. This study introduces a novel hybrid approach, the Forward Recursive Adaptive Model Extraction Technique (FRAME), which combines Forward Selection and Recursive Feature Elimination (RFE) to enhance feature selection across diverse datasets. FRAME integrates the strengths of both methods, balancing exploration and exploitation of features to optimize selection. A comprehensive evaluation of FRAME was conducted against traditional methods such as SelectKBest and Lasso Regression, using high-dimensional, noisy, and heterogeneous datasets. The results demonstrate that FRAME consistently delivers superior predictive performance based on downstream machine learning evaluation metrics. It effectively reduces dimensionality while maintaining robust model performance, making it particularly valuable for applications requiring interpretable and accurate predictions, such as biomedical diagnostics. This study highlights the importance of assessing feature selection methods across varied datasets to ensure their robustness and generalizability. The findings suggest that FRAME has significant potential for further enhancement, particularly through integration with deep learning architectures for adaptive and real-time feature selection in dynamic environments. By advancing feature selection methodologies, FRAME offers a practical and effective solution to improve machine learning applications across multiple domains.