Machine Learning

, Volume 83, Issue 2, pp 163–192

Block-wise construction of tree-like relational features with monotone reducibility and redundancy

Article

DOI: 10.1007/s10994-010-5208-5

Cite this article as:
Kuželka, O. & Železný, F. Mach Learn (2011) 83: 163. doi:10.1007/s10994-010-5208-5

Abstract

We describe an algorithm for constructing a set of tree-like conjunctive relational features by combining smaller conjunctive blocks. Unlike traditional level-wise approaches which preserve the monotonicity of frequency, our block-wise approach preserves monotonicity of feature reducibility and redundancy, 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 atoms, far beyond the reach of state-of-the art propositionalization or inductive logic programming systems.

Keywords

Inductive logic programming Relational machine learning Propositionalization 
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Copyright information

© The Author(s) 2010

Authors and Affiliations

  1. 1.Faculty of Electrical EngineeringCzech Technical University in PraguePragueCzech Republic

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