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.

本研究针对基因表达分类中高维数据和过拟合问题，提出了一种新颖的特征选择算法BOLIMES。该算法结合了Boruta的鲁棒性和LIME的可解释性，系统性地精炼特征子集，保留了最相关的基因，并通过迭代分类评估选择最大化预测准确度的特征组合。研究成果有效平衡了维度降低与高分类性能，为高维基因表达分析提供了强有力的解决方案。

BOLIMES：基于Boruta和LIME优化的基因表达分类特征选择