In the past decade, many researchers have employed various methodologies to combine decisions of multiple classifiers in order to order to improve recognition results. In this article, we will examine the main combination methods that have been developed for different levels of classifier outputs – abstract level, ranked list of classes, and measurements. At the same time, various issues, results, and applications of these methods will also be considered, and these will illustrate the diversity and scope of this research area. Copyright © 2000 Springer-Verlag Berlin Heidelberg.
|Title of host publication||Multiple Classifier Systems: The 1st International Workshop, MCS 2000|
|Editors||Josef KITTLER , Fabio ROLI|
|Place of Publication||Berlin|
|ISBN (Print)||3540677046, 9783540677048, 9783540450146|
|Publication status||Published - 2000|
CitationSuen, C. Y., & Lam, S. W. L. (2000). Multiple classifier combination methodologies for different output levels. In J. Kittler & F. Roli (Eds.), Multiple Classifier Systems: The 1st International Workshop, MCS 2000 (pp. 52-66). Berlin: Springer.
- Recognition rate
- Majority vote
- Combination method
- Rank score
- Handwriting recognition