Copula for Instance-wise Feature Selection and Ranking
This addresses a limitation in neural network-based feature selection for machine learning practitioners, though it is incremental as it builds on existing frameworks.
The paper tackled the problem of feature dependency in instance-wise feature selection by incorporating Gaussian copula to capture correlations, resulting in improved performance and interpretability on synthetic and real datasets.
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.