gBoost
swMATH ID:  42199 
Software Authors:  Saigo, Hiroto; Nowozin, Sebastian; Kadowaki, Tadashi; Kudo, Taku; Tsuda, Koji 
Description:  gBoost: a mathematical programming approach to graph classification and regression. Graph mining methods enumerate frequently appearing subgraph patterns, which can be used as features for subsequent classification or regression. However, frequent patterns are not necessarily informative for the given learning problem. We propose a mathematical programming boosting method (gBoost) that progressively collects informative patterns. Compared to AdaBoost, gBoost can build the prediction rule with fewer iterations. To apply the boosting method to graph data, a branchandbound pattern search algorithm is developed based on the DFS code tree. The constructed search space is reused in later iterations to minimize the computation time. Our method can learn more efficiently than the simpler method based on frequent substructure mining, because the output labels are used as an extra information source for pruning the search space. Furthermore, by engineering the mathematical program, a wide range of machine learning problems can be solved without modifying the pattern search algorithm. 
Homepage:  https://link.springer.com/article/10.1007/s109940085089z 
Keywords:  graph mining; mathematical programming; classification; regression; QSAR 
Related Software:  gSpan; LIBSVM; AFGen; C4.5; Pegasos; AdaBoost.MH; GitHub; GraphSpace; Sub2vec; MoTeX; graph2vec; LMNN; PrefixSpan; LIBLINEAR; UCIml; node2vec; MoSS; clusfind; SSVM; SVMTorch 
Referenced in:  8 Publications 
Standard Articles
1 Publication describing the Software, including 1 Publication in zbMATH  Year 

gBoost: a mathematical programming approach to graph classification and regression. Zbl 1470.68167 Saigo, Hiroto; Nowozin, Sebastian; Kadowaki, Tadashi; Kudo, Taku; Tsuda, Koji 
2009

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Referenced by 30 Authors
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Referenced in 6 Serials
3  Machine Learning 
1  Applied Mathematics and Computation 
1  Journal of Multivariate Analysis 
1  Journal of Classification 
1  Computers & Operations Research 
1  Statistical Analysis and Data Mining 
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