BOLIMES: Boruta and LIME optiMized fEature Selection for Gene Expression Classification
Journal:
arXiv
Published Date:
Feb 18, 2025
Abstract
Gene expression classification is a pivotal yet challenging task in
bioinformatics, primarily due to the high dimensionality of genomic data and
the risk of overfitting. To bridge this gap, we propose BOLIMES, a novel
feature selection algorithm designed to enhance gene expression classification
by systematically refining the feature subset. Unlike conventional methods that
rely solely on statistical ranking or classifier-specific selection, we
integrate the robustness of Boruta with the interpretability of LIME, ensuring
that only the most relevant and influential genes are retained. BOLIMES first
employs Boruta to filter out non-informative genes by comparing each feature
against its randomized counterpart, thus preserving valuable information. It
then uses LIME to rank the remaining genes based on their local importance to
the classifier. Finally, an iterative classification evaluation determines the
optimal feature subset by selecting the number of genes that maximizes
predictive accuracy. By combining exhaustive feature selection with
interpretability-driven refinement, our solution effectively balances
dimensionality reduction with high classification performance, offering a
powerful solution for high-dimensional gene expression analysis.
