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Block-Wise Construction of Acyclic Relational Features with Monotone Irreducibility and Relevancy Properties
This video was recorded at 26th International Conference on Machine Learning (ICML), Montreal 2009. We describe an algorithm for constructing a set of acyclic conjunctive relational features by combining smaller conjunctive blocks. Unlike traditional level-wise approaches which preserve the monotonicity of frequency, our block-wise approach preserves a form of monotonicity of the irreducibility and relevancy feature properties, which are important in propositionalization employed in the context of classification learning. With pruning based on these properties, our block-wise approach efficiently scales to features including tens of first-order literals, far beyond the reach of state-of-the art propositionalization or inductive logic programming systems.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
