提出新特征选择方法，让分类更准且更稳定。
Margin-aware Fuzzy Rough Feature Selection: Bridging Uncertainty Characterization and Pattern Classification
- 结合类别紧凑性与分离度，优化特征筛选
- 在15个数据集上优于6种顶尖算法
- 适合高维数据中提升分类效果的场景
模糊粗糙特征选择（FRFS）是应对高维数据维度灾难的有效手段。通过剔除冗余和无关特征，FRFS可缓解分类器过拟合、提升泛化性能并降低计算开销。然而，现有大多数FRFS算法仅关注降低模式分类中的不确定性，忽视了较低不确定性并不必然带来更好的分类性能，尽管这在文献中常被视为特征选择有效性的关键指标。为弥合不确定性表征与模式分类之间的鸿沟，我们提出一种考虑标签类别紧凑性与分离度的边际感知模糊粗糙特征选择（MAFRFS）框架。MAFRFS不仅能有效降低模式分类任务中的不确定性，还能引导特征选择向更具可分性和判别力的类别结构演进。在15个公开数据集上的大量实验表明，MAFRFS具有高度可扩展性，且显著优于传统FRFS。基于MAFRFS开发的算法在性能上超越六种前沿特征选择方法。
原文摘要 · Abstract (English)
Fuzzy rough feature selection (FRFS) is an effective means of addressing the curse of dimensionality in high-dimensional data. By removing redundant and irrelevant features, FRFS helps mitigate classifier overfitting, enhance generalization performance, and lessen computational overhead. However, most existing FRFS algorithms primarily focus on reducing uncertainty in pattern classification, neglecting that lower uncertainty does not necessarily result in improved classification performance, despite it commonly being regarded as a key indicator of feature selection effectiveness in the FRFS literature. To bridge uncertainty characterization and pattern classification, we propose a Margin-aware Fuzzy Rough Feature Selection (MAFRFS) framework that considers both the compactness and separation of label classes. MAFRFS effectively reduces uncertainty in pattern classification tasks, while guiding the feature selection towards more separable and discriminative label class structures. Extensive experiments on 15 public datasets demonstrate that MAFRFS is highly scalable and more effective than FRFS. The algorithms developed using MAFRFS outperform six state-of-the-art feature selection algorithms.
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