Abstract. We analyze a Relational Neighbor (RN) classifier, a simple relational predictive model that 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. In recent years, we have seen remarkable advances in algorithms for relational learning, especially statistically based algorithms. These algorithms have been developed in a wide variety of different research fields and problem settings. Relational data differ from traditional data in that they violate the instance-independence assumption. Instances can be related, or linked, in various ways. The label of an instance might depend on the instances it is related to either directly or through arbitrarily long chains of relations. This relational structure further complicates matters as it makes it harder, if not impossible, to separate the data cleanly into test and train sets without losing much relational information. Recent work has begun to investigate foundational issues within relational learning, such as the dimensions across which learners can be compared [11, 14, 25] as well as issues of link dependencies [13] . We broaden these investigations by describing a baseline method to which relational learners should be compared when assessing how well they have extracted a useful model from the given relational structure-beyond what can be achieved by looking only at known class labels of related neighbors. Recent probabilistic relational learning algorithms-e.g., Probabilistic Relational Models (PRMs) [16, 10, 27] , Relational Probability Trees (RPTs) [22] and Relational Bayesian Classifiers (RBCs) [23]-search the relational space for useful attributes and relational structure of neighbors (possibly more than one link away). While there are other relational learning algorithms available [7, 9, 6] , we focus in this paper on the three named algorithms. We know from classical machine learning that even very simple statistical methods such as naive Bayes can perform remarkably well even when compared to more complex methods. However, a question that has yet to receive much attention is how much of the performance of relational learners is due to their complexity and how much can
wvRN REF presented a weighted-vote relational neighbor classifier to solve link-based classification problems based solely on the class labels of linked neighbors.
A Simple Relational Classifier
Proceedings of the Second Workshop on Multi-Relational Data Mining (MRDM-2003) at KDD-2003
Computer Science