Employing an Adjusted Stability Measure for Multi-Criteria Model Fitting on Data Sets with Similar Features
This work addresses feature selection problems in machine learning for datasets with correlated features, offering an incremental improvement over existing methods.
The paper tackles the challenge of fitting predictive models that include all relevant features while excluding irrelevant or redundant ones, especially on datasets with similar features, by proposing a multi-criteria tuning approach based on predictive accuracy and feature selection stability. It shows that this approach achieves the same or better predictive performance compared to standard single-criteria tuning and stability selection, effectively selecting relevant features and avoiding irrelevant ones without decreasing accuracy.
Fitting models with high predictive accuracy that include all relevant but no irrelevant or redundant features is a challenging task on data sets with similar (e.g. highly correlated) features. We propose the approach of tuning the hyperparameters of a predictive model in a multi-criteria fashion with respect to predictive accuracy and feature selection stability. We evaluate this approach based on both simulated and real data sets and we compare it to the standard approach of single-criteria tuning of the hyperparameters as well as to the state-of-the-art technique "stability selection". We conclude that our approach achieves the same or better predictive performance compared to the two established approaches. Considering the stability during tuning does not decrease the predictive accuracy of the resulting models. Our approach succeeds at selecting the relevant features while avoiding irrelevant or redundant features. The single-criteria approach fails at avoiding irrelevant or redundant features and the stability selection approach fails at selecting enough relevant features for achieving acceptable predictive accuracy. For our approach, for data sets with many similar features, the feature selection stability must be evaluated with an adjusted stability measure, that is, a measure that considers similarities between features. For data sets with only few similar features, an unadjusted stability measure suffices and is faster to compute.