Feature Expansion With Semi-Dynamic Feature Sets (SDFS) and LLM-Powered Explainability: A Novel Approach to Discovering Missing Features

Traditional feature selection methods focus on reducing dimensionality, often at the cost of discarding valuable but hidden information. In contrast, this paper introduces a feature expansion method, a novel approach that automatically identifies and reconstructs missing features, meaning attributes overlooked or undiscovered during initial feature extraction, enhancing predictive performance without requiring additional data collection. We propose the Semi-Dynamic Feature Set (SDFS) Network, a unique machine learning framework that learns both feature values and model parameters, thereby improving robustness in complex datasets. To enhance interpretability, we integrate Large Language Models (LLMs) to assign meaningful labels to the newly discovered features, transforming them from blackbox variables into actionable insights. Our proposed feature expansion method consistently improved classification and regression performance across multiple datasets. In particular, the accuracy increased from 89% to 95% for Parkinson’s Disease Detection and from 82% to 85% forWine Quality Prediction, and reduced the mean squared error (MAE) in air quality prediction from 91.2 to 85.04, demonstrating its effectiveness in discovering missing features. By combining our feature expansion with LLMdriven explainability, this method not only enhances model’s performance, but also unveils hidden data patterns in critical domains such as healthcare, finance, and scientific research. The full implementation is available on GitHub [GitHub link], and the model can be installed via PyPI under the package ‘sdfs’ [PyPI link].