Efficient rule induction by ignoring pointless rules

TL;DR

This paper presents an ILP method that efficiently prunes redundant rules, significantly reducing learning time while maintaining accuracy across various domains.

Contribution

Introduction of a novel ILP approach that identifies and ignores pointless rules to optimize the hypothesis search process.

Findings

Reduced learning times by up to 99%

Maintained predictive accuracy across multiple domains

Effective pruning of the hypothesis space

Abstract

The goal of inductive logic programming (ILP) is to find a set of logical rules that generalises training examples and background knowledge. We introduce an ILP approach that identifies pointless rules. A rule is pointless if it contains a redundant literal or cannot discriminate against negative examples. We show that ignoring pointless rules allows an ILP system to soundly prune the hypothesis space. Our experiments on multiple domains, including visual reasoning and game playing, show that our approach can reduce learning times by 99% whilst maintaining predictive accuracies.