| Autor: |
Xu, Zhixiang Eddie, Huang, Gao, Weinberger, Kilian Q., Zheng, Alice X. |
| Rok vydání: |
2019 |
| Předmět: |
|
| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
|
