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Reliability estimation of a statistical classifier
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
IEE S.A., ZAE Weiergewan, 5326 Contern, Luxembourg.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-2298-6774
2008 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 29, no 3, 243-253 p.Article in journal (Refereed) Published
Abstract [en]

Statistical pattern classification techniques have been successfully applied to many practical classification problems. In real-world applications, the challenge is often to cope with patterns that lead to unreliable classification decisions. These situations occur either due to unexpected patterns, i.e., patterns which occur in the regions far from the training data or due to patterns which occur in the overlap region of classes. This paper proposes a method for estimating the reliability of a classifier to cope with these situations. While existing methods for quantifying the reliability are often solely based on the class membership probability estimated on global approximations, in this paper, the reliability is quantified in terms of a confidence interval on the class membership probability. The size of the confidence interval is calculated explicitly based on the local density of training data in the neighborhood of a test pattern. A synthetic example is given to illustrate the various aspects of the proposed approach. In addition, experimental evaluation on real data sets is conducted to demonstrate the effectiveness of the proposed approach to detect unexpected patterns. The lower bound of the confidence interval is used to detect the unexpected patterns. By comparing the performance with the state-of-the-art methods, we show our approach is well-founded.

Place, publisher, year, edition, pages
2008. Vol. 29, no 3, 243-253 p.
Keyword [en]
pattern classification, local density estimation, confidence intervals, binomial distribution, GMMs, pattern rejection, pattern-recognition, density functions, error, rule
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-17336DOI: 10.1016/j.patrec.2007.09.019ISI: 000253335100008Scopus ID: 2-s2.0-37049017079OAI: oai:DiVA.org:kth-17336DiVA: diva2:335380
Note
QC 20100525Available from: 2010-08-05 Created: 2010-08-05 Last updated: 2017-12-12Bibliographically approved

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