DocumentCode
2450720
Title
Fusion of one-class classifiers in the belief function framework
Author
Aregui, Astride ; Denoeux, Thierry
Author_Institution
Univ. de Technol. de Compiegne, Compiegne
fYear
2007
fDate
9-12 July 2007
Firstpage
1
Lastpage
8
Abstract
A method is proposed for converting a novelty measure such as produced by one-class SVMs or Kernel principal component analysis (KPCA) into a belief function on a well- defined frame of discernment. This makes it possible to combine one-class classification or novelty detection methods with other information expressed in the same framework such as expert opinions or multi-class classifiers.
Keywords
belief networks; pattern classification; principal component analysis; support vector machines; KPCA; SVM; belief function framework; kernel principal component analysis; novelty detection; one-class classifiers; Kernel; Monitoring; Pattern classification; Principal component analysis; Support vector machine classification; Support vector machines; Demspter-Shafer theory; Novelty detection; Transferable Belief Model; evidence theory; one-class classification;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Fusion, 2007 10th International Conference on
Conference_Location
Quebec, Que.
Print_ISBN
978-0-662-45804-3
Electronic_ISBN
978-0-662-45804-3
Type
conf
DOI
10.1109/ICIF.2007.4408102
Filename
4408102
Link To Document