Selecting Structural Base Classifiers for Graph-Based Multiple Classifier Systems
Selecting a set of good and diverse base classifiers is essential for building multiple classifier systems. However, almost all commonly used procedures for selecting such base classifiers cannot be directly applied to select structural base classifiers. The main reason is that structural data cannot be represented in a vector space.
For graph-based multiple classifier systems, only using subgraphs for building structural base classifiers has been considered so far. However, in theory, a full graph preserves more information than its subgraphs. Therefore, in this work, we propose a different procedure which can transform a labelled graph into a new set of unlabelled graphs and preserve all the linkages at the same time. By embedding the label information into edges, we can further ignore the labels. By assigning weights to the edges according to the labels of their linked nodes, the strengths of the connections are altered, but the topology of the graph as a whole is preserved.
Since it is very difficult to embed graphs into a vector space, graphs are usually classified based on pairwise graph distances. We adopt the dissimilarity representation and build the structural base classifiers based on labels in the dissimilarity space. By combining these structural base classifiers, we can solve the labelled graph classification problem with a multiple classifier system. The performance of using the subgraphs and full graphs to build multiple classifier systems is compared in a number of experiments.
KeywordsLabel Graph Label Information Connection Matrix Feature Subset Selection Full Graph
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