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)
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