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Multiple classifier systems incorporating uncertainty

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vts_7502_10695.pdf (7.535Mb)
205 Seiten
 
Veröffentlichung
2011-01-13
DOI
10.18725/OPARU-3901
Dissertation


Authors
Thiel, Christian
Faculties
Fakultät für Ingenieurwissenschaften und Informatik
License
Standard (ohne Print-On-Demand)
https://oparu.uni-ulm.de/xmlui/license_opod_v1
Abstract
Multiple classifier systems (MCS) unite the answers of separately-trained powerful base-classifiers to obtain the right classification for the sample at hand. In practical applications, a sample is not associated with exactly one class, but belongs fuzzily to multiple ones. How uncertain class information can be incorporated into multiple classifier systems is detailed in this work. On the theoretical side, it is described how existing approaches to modelling uncertainty like Bayesian probability, Dempster-Shafer theory, fuzzy logic or fuzzy sets, and also unfamiliar ones like the distribution of opinions, are able to support and deal with the core notions of uncertainty in classification: vagueness, imprecision and certainty. In the larger practical part, the use of uncertainty is detailed for every stage of the MCS. The most suitable classifiers are identified, and some well-known schemes extended to deal and answer with uncertain class information. Notably every aspect of the newly proposed and award-winning (KES 2007) fuzzy-input fuzzy-output support vector machines is explained. How the certainty of a classifier answer can be quantified is explored as well as which fusion scheme to use to come to a final classification. The steps for applying the techniques above to real-world problems are shown exemplarily for two applications, the recognition of emotions in facial expression videos, and land cover mapping from satellite images (a winner of the IEEE DFTC Contest 2008). Homepage des Autors: http://www.christianthiel.com/dissertation.html
Date created
2010
Original publication
Thiel, Christian: Multiple classifier systems incorporating uncertainty. - München: Verl. Dr. Hut, 2010. - ISBN 978-3-86853-675-1
http://www.hut-verlag.de/9783868536751.html
Subject Headings
Dempster-Shafer-Theorie [GND]
Fuzzy-Integral [GND]
Fuzzy-Maß [GND]
Fuzzy-Wahrscheinlichkeit [GND]
Klassifikation [GND]
Klassifikator <Informatik> [GND]
Sicherheit [GND]
Support-Vektor-Maschine [GND]
Unsicheres Schließen [GND]
Unsicherheit [GND]
Wahrscheinlichkeitstheoretische Menge [GND]
Certainty [LCSH]
Remote sensing [LCSH]
Support vector machines [LCSH]
Uncertainty [LCSH]
Man-machine systems [MeSH]
Keywords
Classifier; Emotion recognition; Emotionserkennung; Fuzzy-input; Imprecision; MCS; Mehrklassifikatorsystem; Multiple classifiers systems; Unsicherheitskalkül; Vagueness
Dewey Decimal Group
DDC 004 / Data processing & computer science

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Thiel, Christian (2011): Multiple classifier systems incorporating uncertainty. Open Access Repositorium der Universität Ulm. Dissertation. http://dx.doi.org/10.18725/OPARU-3901

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