The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
An Inexact Graph Comparison Approach in Joint Eigenspace
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Graph Classification Based on Dissimilarity Space Embedding
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A Labelled Graph Based Multiple Classifier System
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Classifier ensembles for vector space embedding of graphs
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Graph matching – challenges and potential solutions
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Multiple classifiers for graph of words embedding
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
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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.