Learning Tree-Structured Data in the Model Space

As a kind of important structured data, tree is widely used in domains like biology, molecular chemical, and text processing etc. However, many traditional machine learning methods cannot directly deal with tree-structured data. Currently, the commonly adopted approach is based on the subtree. It is supposed that the more common structure between two trees the more similarity between them. To effectively and efficiently learn tree-structured data, this paper proposes to learn tree-structured data using the echo state network based model space. The key idea is to learn models for the complex tree-structured data and convert the original tree data into points in the model space. The similarity between the tree-structured data is then measured by the distance of the models. Kernel method is combined to improve the discrimination performance of the classifier. Experiments are carried out on benchmark data sets to evaluate our proposed approach. Compared with existing methods, experimental results show that the proposed approach achieves better performance and wider application range.