The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
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VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
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ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
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Semi-supervised feature selection using co-occurrent frequent subgraphs
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
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Subgraph patterns are widely used in graph classification, but their effectiveness is often hampered by large number of patterns or lack of discrimination power among individual patterns. We introduce a novel classification method based on pattern co-occurrence to derive graph classification rules. Our method employs a pattern exploration order such that the complementary discriminative patterns are examined first. Patterns are grouped into co-occurrence rules during the pattern exploration, leading to an integrated process of pattern mining and classifier learning. By taking advantage of co-occurrence information, our method can generate strong features by assembling weak features. Unlike previous methods that invoke the pattern mining process repeatedly, our method only performs pattern mining once. In addition, our method produces a more interpretable classifier and shows better or competitive classification effectiveness in terms of accuracy and execution time.