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.
|Original language||American English|
|Number of pages||12|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - 2013|