An Application of Boosting to Graph Classification

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 algorithm 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 convolution kernels as well as improves the testing efficiency.

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Taku Kudo, Eisaku Maeda, Yuji Matsumoto
• Keywords: Chemical Compounds, Convolution Kernels, Decision Stumps, NIPS 2004, NIPS 2007