FEATURE SELECTION AND MODEL OPTIMIZATION IN HIGH-DIMENSIONAL GENOMIC DATA
Abstract
High-dimensional genomic datasets pose unique challenges to predictive modeling due to the curse of dimensionality, multicollinearity, and noise. Feature selection and model optimization strategies are central to enhancing model accuracy, interpretability, and generalization. This paper explores current methodologies including filter, wrapper, and embedded methods for feature selection, along with hyperparameter tuning techniques such as grid search, Bayesian optimization, and genetic algorithms. Through theoretical insights and visual demonstrations, this work synthesizes effective practices in managing high-dimensional genomic data for machine learning models.
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