The gboost toolbox is a framework for classification of connected, undirected, labeled graphs.
The gboost classifiers check for the presence of certain subgraphs in the larger graph. The subgraphs being checked are optimally determined by discriminative subgraph mining. The classification hypotheses is interpretable because only a small number of subgraphs are used to determine the overall classification decision.
The gboost toolbox includes code for the following functionalities:
* Discriminative Subgraph Mining * Frequent Subgraph Mining (gSpan) * Subgraph-Graph isomorphism test (through VFlib) * nu-LPBoost 2-class classifier * nu-LPBoost 1.5-class classifier * simple wrappers to easily train a classifier for graphs
Most of the code is written in C++ with MEX Matlab wrappers.
- Changes to previous version:
Initial Announcement on mloss.org.
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