Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining protein family specific residue packing patterns from protein structure graphs
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Cyclic pattern kernels for predictive graph mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Graph Embedding: A General Framework for Dimensionality Reduction
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
Shortest-Path Kernels on Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Feature selection in a kernel space
Proceedings of the 24th international conference on Machine learning
Spectral feature selection for supervised and unsupervised learning
Proceedings of the 24th international conference on Machine learning
L2 norm regularized feature kernel regression for graph data
Proceedings of the 18th ACM conference on Information and knowledge management
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
It's who you know: graph mining using recursive structural features
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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With the development of highly efficient graph data collection technology in many application fields, classification of graph data emerges as an important topic in the data mining and machine learning community. Towards building highly accurate classification models for graph data, here we present an efficient graph feature selection method. In our method, we use frequent subgraphs as features for graph classification. Different from existing methods, we consider the spatial distribution of the subgraph features in the graph data and select those ones that have consistent spatial location. We have applied our feature selection methods to several cheminformatics benchmarks. Our method demonstrates a significant improvement of prediction as compared to the state-of-the-art methods.