Dual-stage optimizer for systematic overestimation adjustment applied to multi-objective genetic algorithms for biomarker selection

The challenge in biomarker discovery using machine learning from omics data lies in the abundance of molecular features but scarcity of samples. Most feature selection methods in machine learning require evaluating various sets of features (models) to determine the most effective combination. This process, typically conducted using a validation dataset, involves testing different feature sets to optimize the model's performance. Evaluations have performance estimation error and when the selection involves many models the best ones are almost certainly overestimated. Biomarker …

algorithms challenge combination cs.lg data data lies discovery feature features feature selection lies machine machine learning multi-objective process q-bio.qm samples stage