Abstract
A large experiment on combining classifiers is reported and discussed. It includes, both, the combination of different classifiers on the same feature set and the combination of classifiers on different feature sets. Various fixed and trained combining rules are used. It is shown that there is no overall winning combining rule and that bad classifiers as well as bad feature sets may contain valuable information for performance improvement by combining rules. Best performance is achieved by combining both, different feature sets and different classifiers.
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Duin, R.P.W., Tax, D.M.J. (2000). Experiments with Classifier Combining Rules. In: Multiple Classifier Systems. MCS 2000. Lecture Notes in Computer Science, vol 1857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45014-9_2
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DOI: https://doi.org/10.1007/3-540-45014-9_2
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Online ISBN: 978-3-540-45014-6
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