IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A Riemannian approach to graph embedding
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
A Labelled Graph Based Multiple Classifier System
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifier ensembles for vector space embedding of graphs
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Graph of words embedding for molecular structure-activity relationship analysis
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Dimensionality reduction for graph of words embedding
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Selecting structural base classifiers for graph-based multiple classifier systems
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Improving fuzzy multilevel graph embedding through feature selection technique
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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During the last years, there has been an increasing interest in applying the multiple classifier framework to the domain of structural pattern recognition. Constructing base classifiers when the input patterns are graph based representations is not an easy problem. In this work, we make use of the graph embedding methodology in order to construct different feature vector representations for graphs. The graph of words embedding assigns a feature vector to every graph by counting unary and binary relations between node representatives and combining these pieces of information into a single vector. Selecting different node representatives leads to different vectorial representations and therefore to different base classifiers that can be combined. We experimentally show how this methodology significantly improves the classification of graphs with respect to single base classifiers.