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)
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Authors
- 1. Michael R. Anderson (University of Michigan)
BibTeX Citation
@inproceedings{anderson_cidr15,
address = {Amsterdam, Netherlands},
series = {{CIDR} '15},
title = {{Faster Evaluation of Labor-Intensive Features}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Anderson, Michael R.},
year = {2015}
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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 |
|---|---|---|---|---|
| 2,604 | Brainwash: A Data System for Feature Engineering | 2013 | CIDR | 8.3524514e-05 |
| 5,297 | An Integrated Development Environment for Faster Feature Engineering | 2014 | VLDB | 6.2772486e-05 |
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