C4.5: programs for machine learning
C4.5: programs for machine learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Discrete Mathematics
Data on the Web: from relations to semistructured data and XML
Data on the Web: from relations to semistructured data and XML
Transversing itemset lattices with statistical metric pruning
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Optimization by Vector Space Methods
Optimization by Vector Space Methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Linear Programming Boosting via Column Generation
Machine Learning
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Computing Optimal Hypotheses Efficiently for Boosting
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Convex Optimization
Cyclic pattern kernels for predictive graph mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Hierarchical document categorization with support vector machines
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Mining Generalized Substructures from a Set of Labeled Graphs
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Protein function prediction via graph kernels
Bioinformatics
2005 Speical Issue: Graph kernels for chemical informatics
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Simpler knowledge-based support vector machines
ICML '06 Proceedings of the 23rd international conference on Machine learning
Informatics system comprehension: A learner-centred cognitive approach to networked thinking
Education and Information Technologies
Comparison of Descriptor Spaces for Chemical Compound Retrieval and Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A novel support vector classifier with better rejection performance
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Don't be afraid of simpler patterns
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
On the Complexity of Constraint-Based Theory Extraction
DS '09 Proceedings of the 12th International Conference on Discovery Science
Boosting with structure information in the functional space: an application to graph classification
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
NDPMine: efficiently mining discriminative numerical features for pattern-based classification
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Sparse substring pattern set discovery using linear programming boosting
DS'10 Proceedings of the 13th international conference on Discovery science
LGM: mining frequent subgraphs from linear graphs
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Learning from graph data by putting graphs on the lattice
Expert Systems with Applications: An International Journal
Review: Supervised classification and mathematical optimization
Computers and Operations Research
Out-of-bag discriminative graph mining
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Graph classification with imbalanced class distributions and noise
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
International Journal of Knowledge Discovery in Bioinformatics
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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.