The asymmetric dependency measure employed in the group-feature selection method, while useful, has potential limitations. One significant limitation is that it may not adequately capture the full extent of relationships between feature groups, particularly in cases where the dependency is bidirectional. This could lead to the selection of redundant groups that are not truly independent, thereby undermining the effectiveness of the feature selection process.
To address this limitation, alternative measures of dependency can be explored. For instance, symmetric measures such as mutual information can be utilized, which quantify the amount of information shared between two variables, regardless of their order. This could provide a more balanced view of the relationships between feature groups.
Additionally, advanced techniques such as copula-based measures or distance correlation can be investigated. These methods can capture nonlinear dependencies and provide a more comprehensive understanding of the relationships among features. By incorporating these alternative measures, the robustness of the group-feature selection method can be enhanced, leading to improved performance in selecting non-redundant feature groups.