2014 GradientBoostedFeatureSelection

• (Xu et al., 2014) ⇒ Zhixiang Xu, Gao Huang, Kilian Q. Weinberger, and Alice X. Zheng. (2014). “Gradient Boosted Feature Selection.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623635

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Gradient+Boosted+Feature+Selection
• http://dl.acm.org/citation.cfm?id=2623330.2623635&preflayout=flat#citedby

Quotes

Author Keywords

• Feature evaluation and selection; feature selection; gradient boosting; large-scale

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 outperforms other state of the art feature selection algorithms. Yet it scales to larger data set sizes and naturally allows for domain-specific side information.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 GradientBoostedFeatureSelection	Alice X. Zheng Zhixiang Xu Gao Huang Kilian Q. Weinberger			Gradient Boosted Feature Selection				10.1145/2623330.2623635		2014