高维基因数据中，特征选择比模型复杂度更重要。
Feature Dimensionality Outweighs Model Complexity in Breast Cancer Subtype Classification Using TCGA-BRCA Gene Expression Data
- 用不同数量的变异基因训练三种模型，比较分类表现
- 逻辑回归在稀有亚型上表现最稳定，准确率超90%
- 应关注细分亚型性能，而非整体准确率
从基因表达数据准确分类乳腺癌亚型对诊断和治疗选择至关重要。然而，此类数据具有高维度和样本量有限的特点，给机器学习带来挑战。本研究使用TCGA-BRCA基因表达数据，评估模型复杂度与特征选择对亚型分类性能的影响。采用逻辑回归、随机森林和支持向量机（SVM）模型，基于50至20,518个高变基因进行训练。通过分层5折交叉验证评估性能，指标包括准确率和宏F1分数。所有模型均达到较高准确率，但宏F1分析显示各亚型间表现差异显著。逻辑回归在各类亚型中表现最稳定，尤其提升了稀有亚型的检测能力；随机森林虽整体准确率高，但在少数亚型上表现较差；SVM对特征维度敏感。结果强调了模型简单性、评估指标选择及特征筛选在高维生物分类任务中的重要性。
原文摘要 · Abstract (English)
Accurate classification of breast cancer subtypes from gene expression data is critical for diagnosis and treatment selection. However, such datasets are characterized by high dimensionality and limited sample size, posing challenges for machine learning models. In this study, we evaluate the impact of model complexity and feature selection on subtype classification performance using TCGA-BRCA gene expression data. Logistic regression, random forest, and support vector machine (SVM) models were trained using varying numbers of highly variable genes (50 to 20,518). Performance was evaluated using stratified 5-fold cross-validation and assessed with accuracy and macro F1 score. While all models achieved high accuracy, macro F1 analysis revealed substantial differences in subtype-level performance. Logistic regression demonstrated the most stable and balanced performance across subtypes, including improved detection of rare classes. Random forest underperformed on minority subtypes despite strong overall accuracy, while SVM showed sensitivity to feature dimensionality. These findings highlight the importance of model simplicity, evaluation metrics, and feature selection in high-dimensional biological classification tasks.
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