Multiple classifier combination methodologies for different output levels

Ching Y. SUEN, Suk Wah Louisa LAM

Research output: Contribution to conferencePapers

84 Citations (Scopus)

Abstract

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.
Original languageEnglish
Publication statusPublished - Jun 2000

Citation

Suen, C. Y., & Lam, S. W. L. (2000, June). Multiple classifier combination methodologies for different output levels. Paper presented at The First International Workshop on Multiple Classifier System (MCS 2000), Santa Margherita di Pula, Sardinia, Italy.

Fingerprint Dive into the research topics of 'Multiple classifier combination methodologies for different output levels'. Together they form a unique fingerprint.