用瓦斯科尔距离选特征，抗噪能力强，效果更稳定。
Feature Selection Based on Wasserstein Distance
- 基于马尔可夫毯和瓦斯科尔距离评估特征相似性
- 在噪声环境下显著优于传统相关性和KL散度方法
- 无需预设噪声模型，适合真实数据中的不干净标签
本文提出一种基于瓦斯科尔距离的新型特征选择方法，以提升机器学习中的特征选择性能。与依赖相关性或KL散度的传统方法不同，该方法利用瓦斯科尔距离衡量特征相似性，天然捕捉类别关系，对噪声标签具有鲁棒性。我们设计了一种基于马尔可夫毯的特征选择算法，并证明其有效性。分析表明，该方法能有效降低噪声标签的影响，且无需依赖特定噪声模型。我们给出了其有效性的下界，在噪声存在时依然有意义。多组实验结果表明，该方法在多个数据集上均优于传统方法，尤其在噪声场景中表现突出。
原文摘要 · Abstract (English)
This paper presents a novel feature selection method leveraging the Wasserstein distance to improve feature selection in machine learning. Unlike traditional methods based on correlation or Kullback-Leibler (KL) divergence, our approach uses the Wasserstein distance to assess feature similarity, inherently capturing class relationships and making it robust to noisy labels. We introduce a Markov blanket-based feature selection algorithm and demonstrate its effectiveness. Our analysis shows that the Wasserstein distance-based feature selection method effectively reduces the impact of noisy labels without relying on specific noise models. We provide a lower bound on its effectiveness, which remains meaningful even in the presence of noise. Experimental results across multiple datasets demonstrate that our approach consistently outperforms traditional methods, particularly in noisy settings.
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