Abstract
In this paper, we address the problem of edge selection for networked data, that is, given a set of interlinked entities for which many different kinds of links can be defined, how do we select those links that lead to a better classification of the dataset. We evaluate the current approaches to the edge selection problem for relational classification. These approaches are based on defining a metric over the graph that quantifies the goodness of a specific link type. We propose a new metric to achieve this very same goal. Experimental results show that our proposed metric outperforms the existing ones.
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Pérez-Solà, C., Herrera-Joancomartí, J. (2013). Improving Automatic Edge Selection for Relational Classification. In: Torra, V., Narukawa, Y., Navarro-Arribas, G., Megías, D. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2013. Lecture Notes in Computer Science(), vol 8234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41550-0_25
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DOI: https://doi.org/10.1007/978-3-642-41550-0_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41549-4
Online ISBN: 978-3-642-41550-0
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