Twin Boosting: improved feature selection and prediction |
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Authors: | Peter Bühlmann Torsten Hothorn |
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Institution: | (1) Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA |
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Abstract: | We propose Twin Boosting which has much better feature selection behavior than boosting, particularly with respect to reducing
the number of false positives (falsely selected features). In addition, for cases with a few important effective and many
noise features, Twin Boosting also substantially improves the predictive accuracy of boosting. Twin Boosting is as general
and generic as (gradient-based) boosting. It can be used with general weak learners and in a wide variety of situations, including
generalized regression, classification or survival modeling. Furthermore, it is computationally feasible for large problems
with potentially many more features than observed samples. Finally, for the special case of orthonormal linear models, we
prove equivalence of Twin Boosting to the adaptive Lasso which provides some theoretical aspects on feature selection with
Twin Boosting. |
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Keywords: | |
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