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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
6175
Venue
SIGMOD
Year
2021
Pagerank
7.4589576e-05
Overall Rank
3,162 | 78.01%
DOI
10.1145/3448016.3457284

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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
181 Mining Frequent Patterns without Candidate Generation 2000 SIGMOD 0.00036992674
2,259 MithraCoverage: A System for Investigating Population Bias for Intersectional Fairness 2020 SIGMOD 9.167331e-05
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