Abstract
Big biological datasets, such as gene expression profiles, often contain redundant features that degrade model performance and limit generalization across independent datasets with complexities like class imbalance and hidden sub-clusters. To overcome challenges, we present 'FLASH', a novel feature selection method combining filtration and heuristic-based systematic elimination. FLASH generates random samples and computes p-values for each feature using multiple statistical tests (t-test, ANOVA, Wilcoxon Rank-Sum, Brunner-Munzel, Mann-Whitney). Features are scored by aggregating significant p-values across samples. The coefficient from the machine learning model with the highest accuracy on the filtered features is used to rank them. Recursive elimination with cross-validation systematically removes features while monitoring accuracy. The final subset is selected based on the highest performance during elimination, to achieve effective feature selection. We show that our method preserves predictive performance on independent datasets. Our comprehensive evaluation across diverse datasets showed that FLASH outperforms the compared feature selection methods dRFE, Mutual information, MRMR, ElasticNet, NeuralNet, Permutation test and SAGA within the scope of our tested datasets and evaluation settings. Additionally, features selected by FLASH demonstrated greater biological relevance, as evidenced by higher overlap with disease-associated genes from DisGeNET in an independent dataset.