Selection of Classifiers for the Construction of Multiple Classifier Systems
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Using diversity of errors for selecting members of a committee classifier
Pattern Recognition
Multiple classifier systems in remote sensing: from basics to recent developments
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Speeding up graph edit distance computation through fast bipartite matching
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Multiple classifiers for graph of words embedding
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Towards the unification of structural and statistical pattern recognition
Pattern Recognition Letters
Selecting structural base classifiers for graph-based multiple classifier systems
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
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In general, classifying graphs with labelled nodes (also known as labelled graphs) is a more difficult task than classifying graphs with unlabelled nodes. In this work, we decompose the labelled graphs into unlabelled subgraphs with respect to the labels, and describe these decomposed subgraphs with the travelling matrices. By utilizing the travelling matrices to calculate the dissimilarity for all pairs of subgraphs with the JoEig approach [6], we can build a base classifier in the dissimilarity space for each label. By combining these label base classifiers with the global structure base classifiers built on dissimilarities of graphs considering the full adjacency matrices and the full travelling matrices, respectively, we can solve the labelled graph classification problem with the multiple classifier system.