Block-wise construction of acyclic relational features with monotone irreducibility and relevancy properties

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 blockwise 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.

• Type: Conference
• Year: 2009
• Where: ICML
• Authors: Filip Zelezný, Ondrej Kuzelka
• Keywords: Conjunctive Relational Features, ICML 2009, Machine Learning, Relevancy Feature Properties, Smaller Conjunctive Blocks