A Simple Relational Classifier

  • Sofus Macskassy
  • Foster Provost

We analyze a Relational Neighbor (RN) classifier, a simple relational predictive model the predicts only based on class labels of related neighbors, using no learning and no inherent attributes.  We show that it performs surprisingly well by comparing it to more complex models such as Probabilistic Relational Models and Relational Probability Trees on three data sets from published work.  We argue that a simple model such as this should be used as a baseline to assess the performance of relational learners.

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