BOLIMES: Boruta and LIME optiMized fEature Selection for Gene Expression Classification
This work addresses the problem of overfitting in gene expression classification for bioinformatics, though it appears incremental as it combines existing methods.
The paper tackled the challenge of high-dimensional gene expression classification by proposing BOLIMES, a feature selection algorithm that integrates Boruta and LIME to refine gene subsets, resulting in enhanced predictive accuracy for genomic data.
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.