Gradient Boosted Feature Selection [article]

Zhixiang Eddie Xu, Gao Huang, Kilian Q. Weinberger, Alice X. Zheng
2019 arXiv   pre-print
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
more » ... lement 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.
arXiv:1901.04055v1 fatcat:eq6mw6f4dndrjeh4s4x5k6vwmu