Robust function discovery and feature selection for life sciences and engineering
Mark E. Kotanchek · 2012
Industrial process and product optimization is impossible without meaningful models and insights on significant features controlling process or product performance. Real-world modeling and feature selection problems have many issues - high-dimensional, non-linear, with unbalanced measurements, correlated features, missing experiments, etc., which makes it difficult for most people to know what the right approach is in any given situation. We present a function discovery technology based on symbolic regression that routinely converts these problems into meaningful and insightful models with robust driver features identification. Without requiring a Ph.D. in Computer Science or Statistics, it is now possible to easily develop robust nonlinear models (complete with trust measures), identify data outliers and interactively explore the model dynamics and response sensitivities.