Optimization by Vector Space Methods
Optimization by Vector Space Methods
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
Identifying bug signatures using discriminative graph mining
Proceedings of the eighteenth international symposium on Software testing and analysis
IEEE Transactions on Knowledge and Data Engineering
Exploratory undersampling for class-imbalance learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Graph classification by means of Lipschitz embedding
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
Semi-supervised feature selection for graph classification
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph classification: a diversified discriminative feature selection approach
Proceedings of the 21st ACM international conference on Information and knowledge management
Graph stream classification using labeled and unlabeled graphs
ICDE '13 Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)
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Recent years have witnessed an increasing number of applications involving data with structural dependency and graph representations. For these applications, it is very common that their class distribution is imbalanced with minority samples being only a small portion of the population. Such imbalanced class distributions impose significant challenges to the learning algorithms. This problem is further complicated with the presence of noise or outliers in the graph data. In this paper, we propose an imbalanced graph boosting algorithm, igBoost, that progressively selects informative subgraph patterns from imbalanced graph data for learning. To handle class imbalance, we take class distributions into consideration to assign different weight values to graphs. The distance of each graph to its class center is also considered to adjust the weight to reduce the impact of noisy graph data. The weight values are integrated into the iterative subgraph feature selection and margin learning process to achieve maximum benefits. Experiments on real-world graph data with different degrees of class imbalance and noise demonstrate the algorithm performance.