Improving Relational Classification Using Link Prediction Techniques
Abstract
In this paper, we address the problem of classifying entities belonging to networked datasets. We show that assortativity is positively correlated with classification performance and how we are able to improve classification accuracy by increasing the assortativity of the network. Our method to increase assortativity is based on modifying the weights of the edges using a scoring function. We evaluate the ability of different functions to serve for this purpose. Experimental results show that, for the appropriated functions, classification on networks with modified weights outperforms the classification using the original weights.
Cite
Text
Pérez-Solà and Herrera-Joancomartí. "Improving Relational Classification Using Link Prediction Techniques." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013. doi:10.1007/978-3-642-40988-2_38Markdown
[Pérez-Solà and Herrera-Joancomartí. "Improving Relational Classification Using Link Prediction Techniques." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013.](https://mlanthology.org/ecmlpkdd/2013/perezsola2013ecmlpkdd-improving/) doi:10.1007/978-3-642-40988-2_38BibTeX
@inproceedings{perezsola2013ecmlpkdd-improving,
title = {{Improving Relational Classification Using Link Prediction Techniques}},
author = {Pérez-Solà, Cristina and Herrera-Joancomartí, Jordi},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2013},
pages = {590-605},
doi = {10.1007/978-3-642-40988-2_38},
url = {https://mlanthology.org/ecmlpkdd/2013/perezsola2013ecmlpkdd-improving/}
}