Building Classifier Ensembles Using Greedy Graph Edit Distance
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Publication date
2015
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04B - Conference paper
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Multiple Classifier Systems - 12th International Workshop, MCS 2015, Günzburg, Germany, June 29 - July 1, 2015
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Lecture Notes in Computer Science
Series number
9132
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Issue / Number
Pages / Duration
125-134
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Springer
Place of publication / Event location
Hamburg
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Abstract
Classifier ensembles aim at more accurate classifications than single classifiers. In the present paper we introduce a general approach to building structural classifier ensembles, i.e. classifiers that make use of graphs as representation formalism. The proposed methodology is based on a recent graph edit distance approximation. The major observation that motivates the use of this particular approximation is that the resulting distances crucially depend on the order of the nodes of the underlying graphs. Our novel methodology randomly permutes the node order N times such that the procedure leads to N different distance approximations. Next, a distance based classifier is trained for each approximation and the results of the individual classifiers are combined in an appropriate way. In several experimental evaluations we make investigations on the classification accuracy of the resulting classifier ensemble and compare it with two single classifier systems.
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978-3-319-20247-1
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Language
German
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Yes
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Published
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Citation
Riesen, K., Ferrer, M., & Fischer, A. (2015). Building Classifier Ensembles Using Greedy Graph Edit Distance. Multiple Classifier Systems - 12th International Workshop, MCS 2015, Günzburg, Germany, June 29 - July 1, 2015, 125–134. http://hdl.handle.net/11654/10146