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Computing Precision and Recall with Missing or Uncertain Ground Truth

Bart Lamiroy 1 Tao Sun 2 
1 QGAR - Querying Graphics through Analysis and Recognition
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : In this paper we present a way to use precision and recall measures in total absence of ground truth. We develop a probabilistic interpretation of both measures and show that, provided a sufficient number of data sources are available, it offers a viable performance measure to compare methods if no ground truth is available. This paper also shows the limitations of the approach, in case a systematic bias is present in all compared methods, but shows that it maintains a very high level of overall coherence and stability. It opens broader perspectives and can be extended to handling partial or unreliable ground truth, as well as levels of prior confidence in the methods it aims to compare.
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Submitted on : Friday, January 18, 2013 - 10:01:05 PM
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Bart Lamiroy, Tao Sun. Computing Precision and Recall with Missing or Uncertain Ground Truth. Young-Bin Kwon and Jean-Marc Ogier. Graphics Recognition. New Trends and Challenges. 9th International Workshop, GREC 2011, Seoul, Korea, September 15-16, 2011, Revised Selected Papers, 7423, Springer, pp.149-162, 2013, Lecture Notes in Computer Science, 978-3-642-36823-3. ⟨10.1007/978-3-642-36824-0_15⟩. ⟨hal-00778188⟩



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