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- W2949803778 abstract "A feature selection algorithm should ideally satisfy four conditions: reliably extract relevant features; be able to identify non-linear feature interactions; scale linearly with the number of features and dimensions; allow the incorporation of known sparsity structure. In this work we propose a novel feature selection algorithm, Gradient Boosted Feature Selection (GBFS), which satisfies all four of these requirements. The algorithm is flexible, scalable, and surprisingly straight-forward to implement as it is based on a modification of Gradient Boosted Trees. We evaluate GBFS on several real world data sets and show that it matches or out-performs other state of the art feature selection algorithms. Yet it scales to larger data set sizes and naturally allows for domain-specific side information." @default.
- W2949803778 created "2019-06-27" @default.
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- W2949803778 date "2019-01-13" @default.
- W2949803778 modified "2023-09-23" @default.
- W2949803778 title "Gradient Boosted Feature Selection" @default.
- W2949803778 hasPublicationYear "2019" @default.
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