Block-Wise Construction of Acyclic Relational Features with Monotone Irreducibility and Relevancy Properties
Abstract
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.
Cite
Text
Kuzelka and Zelezný. "Block-Wise Construction of Acyclic Relational Features with Monotone Irreducibility and Relevancy Properties." International Conference on Machine Learning, 2009. doi:10.1145/1553374.1553448Markdown
[Kuzelka and Zelezný. "Block-Wise Construction of Acyclic Relational Features with Monotone Irreducibility and Relevancy Properties." International Conference on Machine Learning, 2009.](https://mlanthology.org/icml/2009/kuzelka2009icml-block/) doi:10.1145/1553374.1553448BibTeX
@inproceedings{kuzelka2009icml-block,
title = {{Block-Wise Construction of Acyclic Relational Features with Monotone Irreducibility and Relevancy Properties}},
author = {Kuzelka, Ondrej and Zelezný, Filip},
booktitle = {International Conference on Machine Learning},
year = {2009},
pages = {569-576},
doi = {10.1145/1553374.1553448},
url = {https://mlanthology.org/icml/2009/kuzelka2009icml-block/}
}