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

Statistica Sinica, 2023-01, Vol.33 (1), p.259-279 [Peer Reviewed Journal]

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Title:
Feature-weighted elastic net: using "features of features" for better prediction
Author: Tay, J Kenneth ; Aghaeepour, Nima ; Hastie, Trevor ; Tibshirani, Robert
Is Part Of: Statistica Sinica, 2023-01, Vol.33 (1), p.259-279
Description: 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 ("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.
Publisher: China (Republic : 1949- )
Language: English
Identifier: ISSN: 1017-0405
DOI: 10.5705/ss.202020.0226
PMID: 37102071
Source: Alma/SFX Local Collection

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