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Identifying Insufficient Data Coverage for Ordinal Continuous-Valued Attributes

Summary: Defines coverage for ordinal/continuous attributes as regions with enough similar training data to guarantee accuracy. Presents a Voronoi-diagram-based algorithm to identify uncovered regions in low dimensions and a randomized approximation for high dimensions, validated on real datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6205
Venue
SIGMOD
Year
2021
Pagerank
4.8925683e-05
Overall Rank
6,892 | 52.06%
DOI
10.1145/3448016.3457315

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