Employing an Adjusted Stability Measure for Multi-Criteria Model Fitting on Data Sets with Similar Features

TL;DR

This paper introduces a multi-criteria hyperparameter tuning method that balances predictive accuracy and feature stability, especially effective for datasets with highly correlated features, outperforming traditional single-criteria and stability selection methods.

Contribution

The paper proposes an adjusted stability measure for hyperparameter tuning that improves feature selection stability without sacrificing predictive accuracy in datasets with similar features.

Findings

Achieves equal or better predictive performance than existing methods.

Effectively selects relevant features while avoiding irrelevant or redundant ones.

Adjusted stability measure is crucial for datasets with many similar features.

Abstract

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.