### Abstract

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 branch-and-bound 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.

Original language | English |
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Pages (from-to) | 69-89 |

Number of pages | 21 |

Journal | Machine Learning |

Volume | 75 |

Issue number | 1 |

DOIs | |

Publication status | Published - Apr 1 2009 |

Externally published | Yes |

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### All Science Journal Classification (ASJC) codes

- Software
- Artificial Intelligence

### Cite this

*Machine Learning*,

*75*(1), 69-89. https://doi.org/10.1007/s10994-008-5089-z

**GBoost : A mathematical programming approach to graph classification and regression.** / Saigo, Hiroto; Nowozin, Sebastian; Kadowaki, Tadashi; Kudo, Taku; Tsuda, Koji.

Research output: Contribution to journal › Article

*Machine Learning*, vol. 75, no. 1, pp. 69-89. https://doi.org/10.1007/s10994-008-5089-z

}

TY - JOUR

T1 - GBoost

T2 - A mathematical programming approach to graph classification and regression

AU - Saigo, Hiroto

AU - Nowozin, Sebastian

AU - Kadowaki, Tadashi

AU - Kudo, Taku

AU - Tsuda, Koji

PY - 2009/4/1

Y1 - 2009/4/1

N2 - 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 branch-and-bound 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.

AB - 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 branch-and-bound 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.

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UR - http://www.scopus.com/inward/citedby.url?scp=60949105177&partnerID=8YFLogxK

U2 - 10.1007/s10994-008-5089-z

DO - 10.1007/s10994-008-5089-z

M3 - Article

AN - SCOPUS:60949105177

VL - 75

SP - 69

EP - 89

JO - Machine Learning

JF - Machine Learning

SN - 0885-6125

IS - 1

ER -