Faster Evaluation of Labor-Intensive Features

Summary: Speeds iterative feature engineering by selecting small, informative subsets so costly feature functions needn't run over entire corpora, reducing engineer downtime. Uses one-time clustering + indexing and an online mapping from clusters to model state to pick nonredundant, relevant inputs, yielding 3–10× faster training-set generation. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 231
Venue: CIDR
Year: 2015
Pagerank: -
Overall Rank: 13,360 | 7.06%
DOI: -

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Authors

1. Michael R. Anderson

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Rank	Citing Paper	Year	Venue	Pagerank

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
2,915	Brainwash: A Data System for Feature Engineering	2013	CIDR	7.9078385e-05
6,115	An Integrated Development Environment for Faster Feature Engineering	2014	VLDB	5.2042468e-05

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