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Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence

Summary: Proposes divergence over itemsets to quantify classifier behavior gaps in data subgroups via pattern mining. Shapley-value attribution quantifies each feature's contribution to divergence, enabling detection of critical/peculiar subgroups for validation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6237
Venue
SIGMOD
Year
2021
Pagerank
7.6532441e-05
Overall Rank
3,190 | 78.12%
DOI
10.1145/3448016.3457284

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{pastor_sigmod21,
        title = {{Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence}},
        author = {Pastor, Eliana and de Alfaro, Luca and Baralis, Elena},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457284},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457284},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by 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
161 Mining Frequent Patterns without Candidate Generation 2000 SIGMOD 0.00027981772
3,091 MithraCoverage: A System for Investigating Population Bias for Intersectional Fairness 2020 SIGMOD 7.7684166e-05
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