Randomization Tests for Relational Learning

D. Jensen and J. Neville (2003). Randomization tests for relational learning. Technical Report 03-05. Department of Computer Science. University of Massachusetts Amherst.

Abstract
Algorithms for relational learning and propositional learning face different statistical challenges. In contrast to propositional learners, relational learners often make statistical inferences about data that exhibit linkage and autocorrelation. Recent work has shown that these characteristics of relational data can bias inferences made by relational learners. In this paper, we develop a novel variant of a known statistical procedure — a randomization test — that produces accurate hypothesis tests for relational data. We show that our procedure produces unbiased inferences in situations where more obvious adaptations of existing randomization tests fail.
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