An Application of Boosting to Graph Classification
Abstract
This paper presents an application of Boosting for classifying labeled graphs, general structures for modeling a number of real-world data, such as chemical compounds, natural language texts, and bio sequences. The proposal consists of i) decision stumps that use subgraph as features, and ii) a Boosting algorithm in which subgraph-based decision stumps are used as weak learners. We also discuss the relation between our al- gorithm and SVMs with convolution kernels. Two experiments using natural language data and chemical compounds show that our method achieves comparable or even better performance than SVMs with convo- lution kernels as well as improves the testing efficiency.
Cite
Text
Kudo et al. "An Application of Boosting to Graph Classification." Neural Information Processing Systems, 2004.Markdown
[Kudo et al. "An Application of Boosting to Graph Classification." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/kudo2004neurips-application/)BibTeX
@inproceedings{kudo2004neurips-application,
title = {{An Application of Boosting to Graph Classification}},
author = {Kudo, Taku and Maeda, Eisaku and Matsumoto, Yuji},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {729-736},
url = {https://mlanthology.org/neurips/2004/kudo2004neurips-application/}
}