Classifier fusion using local confidence

Eunju Kim, Wooju Kim, Yillbyung Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Combined classifiers can show better performance than the best single classifier used in isolation, while involving little additional computational effort. This is because different classifier can potentially offer complementary information about the pattern and group decisions can take the advantage of the benefit of combining multiple classifiers in making final decision. In this paper we propose a new combining method, which harness the local confidence of each classifier in the combining process. This method learns the local confidence of each classifier using training data and if an unknown data is given, the learned knowledge is used to evaluate the outputs of individual classifiers. An empirical evaluation using five real data sets has shown that this method achieves a promising performance and outperforms the best single classifiers and other known combining methods we tried.

Original languageEnglish
Title of host publicationFoundations of Intelligent Systems - 13th International Symposium, ISMIS 2002, Proceedings
EditorsMohand-Said Hacid, Zbigniew W. Ras, Djamel A. Zighed, Yves Kodratoff
PublisherSpringer Verlag
Number of pages9
ISBN (Print)3540437851, 9783540437857
Publication statusPublished - 2002
Event13th International Symposium on Methodologies for Intelligent Systems, ISMIS 2002 - Lyon, France
Duration: 2002 Jun 272002 Jun 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2366 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other13th International Symposium on Methodologies for Intelligent Systems, ISMIS 2002

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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