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A Relational Framework for Classifier Engineering

Summary: Formal relational framework modeling feature engineering as database queries to enable DB-centric analysis of classifier expressivity and learnability. Defines separability, VC-dimension of query-defined feature classes, and identifiability; analyzes complexity for conjunctive-query features under common syntactic restrictions. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1713
Venue
PODS
Year
2017
Pagerank
6.0829486e-05
Overall Rank
5,804 | 60.19%
DOI
10.1145/3034786.3034797

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{kimelfeld_pods17,
        address = {New York, NY, USA},
        series = {{PODS} '17},
        title = {{A Relational Framework for Classifier Engineering}},
        url = {https://dl.acm.org/doi/10.1145/3034786.3034797},
        doi = {10.1145/3034786.3034797},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Kimelfeld, Benny and Ré, Christopher},
        year = {2017}
}

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
2,927 In-Database Learning with Sparse Tensors 2018 PODS 7.9531195e-05
10,557 Database Views as Explanations for Relational Deep Learning 2026 VLDB 5.093636e-05
10,622 Towards Efficient Random-Order Enumeration for Join Queries 2026 VLDB 5.093636e-05
11,363 Extremal Fitting Problems for Conjunctive Queries 2023 PODS 5.093636e-05
11,833 Regularizing Conjunctive Features for Classification 2019 PODS 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

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

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