Feature-weighted elastic net: using "features of features" for better prediction

TL;DR

The paper introduces fwelnet, a novel elastic net variant that incorporates additional feature information to improve prediction accuracy and feature selection, outperforming standard methods like lasso.

Contribution

It proposes a new method leveraging 'features of features' to adapt penalties in elastic net, enhancing prediction and feature selection performance.

Findings

fwelnet outperforms lasso in test mean squared error

fwelnet improves true and false positive rates in feature selection

fwelnet achieves higher AUC in preeclampsia prediction

Abstract

In some supervised learning settings, the practitioner might have additional information on the features used for prediction. We propose a new method which leverages this additional information for better prediction. The method, which we call the feature-weighted elastic net ("fwelnet"), uses these "features of features" to adapt the relative penalties on the feature coefficients in the elastic net penalty. In our simulations, fwelnet outperforms the lasso in terms of test mean squared error and usually gives an improvement in true positive rate or false positive rate for feature selection. We also apply this method to early prediction of preeclampsia, where fwelnet outperforms the lasso in terms of 10-fold cross-validated area under the curve (0.86 vs. 0.80). We also provide a connection between fwelnet and the group lasso and suggest how fwelnet might be used for multi-task learning.