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Decision Trees for Entity Identification: Approximation Algorithms and Hardness Results

Summary: Extends entity-identification decision trees beyond binary/uniform inputs to arbitrary attribute alphabets and priors, minimizing expected test cost. Greedy yields O(r_K·log N) (r_K≤log K); Ω(log N) hardness even for K=2 so greedy is binary-optimal up to constants; connects to an Erdős Ramsey conjecture. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1415
Venue
PODS
Year
2007
Pagerank
-
Overall Rank
13,584 | 5.50%
DOI
-

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12,368 Helping Satisfy Multiple Objectives During a Service Desk Conversation 2008 SIGMOD 4.1945683e-05
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