Fast Estimation of Relational Pattern Coverage through Randomization and Maximum Likelihood
published: July 28, 2008, recorded: July 2008, views: 107
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In inductive logic programming, theta-subsumption is a widely used coverage test. Unfortunately, testing theta-subsumption is NP-complete, which represents a crucial efficiency bottleneck for many relational learners. In this paper, we present a probabilistic estimator of clause coverage, based on a randomized restarted search strategy. Under a distribution assumption, our algorithm can estimate clause coverage without having to decide subsumption for all examples. We implement this algorithm in program ReCovEr. On generated graph data and real-world datasets, we show that ReCovEr provides reasonably accurate estimates while achieving dramatic runtimes improvements compared to a state-of-the-art algorithm
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