论文标题
功能加权弹性网：使用“功能的功能”来更好地预测
Feature-weighted elastic net: using "features of features" for better prediction
论文作者
论文摘要
在某些有监督的学习设置中，从业者可能会提供有关预测功能的其他信息。我们提出了一种新方法，该方法利用此其他信息以进行更好的预测。我们称之为特征加权的弹性网（“ FWELNET”）的方法使用这些“功能的特征”来调整弹性网惩罚中特征系数的相对惩罚。在我们的模拟中，FWELNET在测试平方误差方面优于套索，通常可以提高真正的正速率或特征选择的假正率。我们还将这种方法应用于先兆子痫的早期预测，在曲线下，FWELNET以10倍的交叉验证面积优于套索（0.86 vs. 0.80）。我们还提供了FWELNET和组套索之间的联系，并建议如何将FWELNET用于多任务学习。
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.
