Feature Set

Feature sets, collections of data attributes used in machine learning, are central to model performance and interpretability. Current research focuses on optimizing feature selection methods—including information gain, chi-squared tests, and recursive feature elimination—to improve model efficiency and accuracy across diverse applications, from cybersecurity to medical imaging. This involves developing techniques for handling high-dimensional data, managing multiple feature views, and enhancing explainability through methods like Shapley value analysis and weighted variations. Improved feature sets lead to more accurate and efficient models, impacting fields ranging from predictive healthcare to archaeological analysis.