用于实例级特征选择和排序的Copula
Copula for Instance-wise Feature Selection and Ranking
AI总结：
针对现有实例级特征选择方法忽略特征间依赖性的问题，提出将Gaussian copula融入当前框架以捕获变量相关性，实验证明该方法能有效捕获有意义的相关性并提升性能与可解释性。
摘要：
实例级特征选择和排序方法能在神经网络背景下为每个样本实现任务友好特征的良好选择。然而，现有假设特征子集相互独立的方法在考虑特征间依赖性时存在缺陷。为解决这一局限，我们提出将Gaussian copula（一种用于捕获变量间相关性的强大数学技术）融入当前的特征选择框架中，且无需额外修改。在合成和真实数据集上的实验结果表明，无论在性能比较还是可解释性方面，我们的方法均能捕获有意义的相关性。
英文摘要：
Instance-wise feature selection and ranking methods can achieve a good selection of task-friendly features for each sample in the context of neural networks. However, existing approaches that assume feature subsets to be independent are imperfect when considering the dependency between features. To address this limitation, we propose to incorporate the Gaussian copula, a powerful mathematical technique for capturing correlations between variables, into the current feature selection framework with no additional changes needed. Experimental results on both synthetic and real datasets, in terms of performance comparison and interpretability, demonstrate that our method is capable of capturing meaningful correlations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
