An experimental comparison of fixed and trained fusion rules for crisp classifiers outputs

TitleAn experimental comparison of fixed and trained fusion rules for crisp classifiers outputs
Publication TypeConference Paper
Year of Publication2002
AuthorsRoli, F, Raudys, S, Marcialis, GL
EditorF. Kittler, R;J
Conference Name3rd International Workshop on Multiple Classifiers Systems (MCS02)
Volume2364
Pagination232-241
PublisherSpringer
Conference LocationCagliari (Italy)
Keywordsbiometrics, combining rules, mcs01, Multiple Classifier Systems
Abstract

At present, fixed rules for classifier combination are the most used and widely investigated ones, while the study and application of trained rules has received much less attention. Therefore, pros and cons of fixed and trained rules are only partially known even if one focuses on crisp classifier outputs. In this paper, we report the results of an experimental comparison of well-known fixed and trained rules for crisp classifier outputs. Reported experiments allow one draw some preliminary conclusions about comparative advantages of fixed and trained fusion rules.

Citation Key 176