Confidence estimation in classification decision: A method for detecting unseen patterns
2007 (English)In: Proceedings of the Sixth International Conference on Advances in Pattern Recognition, SINGAPORE: WORLD SCIENTIFIC PUBL CO PTE LTD , 2007, 290-294 p.Conference paper (Other academic)
The classification task for a real world application shall include a confidence estimation to handle unseen patterns i.e., patterns which were not considered during the learning stage of a classifier. This is important especially for safety critical applications where the goal is to assign these situations as "unknown" before they can lead to a false classification. Several methods were proposed in the past which were based on choosing a threshold on the estimated class membership probability. In this paper we extend the use of Gaussian mixture model (GMM)to estimate the uncertainty of the estimated class membership probability in terms of confidence interval around the estimated class membership probability. This uncertainty measure takes into account the number of training patterns available in the local neighborhood of a test pattern. Accordingly, the lower bound of the confidence interval or the number of training samples around a test pattern, can be used to detect the unseen patterns. Experimental results on a real-world application are discussed.
Place, publisher, year, edition, pages
SINGAPORE: WORLD SCIENTIFIC PUBL CO PTE LTD , 2007. 290-294 p.
pattern classification, confidence based classifier, density estimation, confidence intervals
IdentifiersURN: urn:nbn:se:kth:diva-34944ISI: 000245470300048OAI: oai:DiVA.org:kth-34944DiVA: diva2:427458
6th International Conference on Advances in Pattern Recognition Indian Stat Inst, Calcutta, INDIA, JAN 02-04, 2007
QC 201106282011-06-282011-06-172016-05-23Bibliographically approved