Fast estimation of first-order clause coverage through randomization and maximum likelihood

In inductive logic programming, subsumption is a widely used coverage test. Unfortunately, testing -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.

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Filip Zelezný, Ondrej Kuzelka
• Keywords: Clause Coverage, Dramatic Runtimes Improvements, ICML 2008, Machine Learning, Randomized Restarted Search