Adaptive Machine Learning Feature Selection Pipeline

Construct an automated feature selection framework that can dynamically evaluate and prune machine learning features based on evolving dataset characteristics. The pipeline should support multiple selection algorithms, handle high-dimensional data, and provide interpretable metrics for feature importance. Implement techniques for handling feature drift, correlation analysis, and computational efficiency. Include methods for generating synthetic feature representations and automated feature engineering.