Combining classifiers

We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental...

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Bibliographic Details
Published inProceedings of 13th International Conference on Pattern Recognition Vol. 2; pp. 897 - 901 vol.2
Main Authors Kittler, J., Hater, M., Duin, R.P.W.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1996
Subjects
Decision making
Estimation error
Extraterrestrial measurements
Machinery
Pattern recognition
Physics
Sensitivity analysis
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ISBN9780818672828
081867282X
ISSN1051-4651
DOI10.1109/ICPR.1996.547205

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Summary:We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental comparison of various classifier combination schemes demonstrates that the combination rule developed under the most restrictive assumptions-the sum rule-and its derivatives consistently outperform other classifier combinations schemes. A sensitivity analysis of the various schemes to estimation errors is carried out to show that this finding can be justified theoretically.
ISBN:9780818672828
081867282X
ISSN:1051-4651
DOI:10.1109/ICPR.1996.547205