Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning

TL;DR

This paper introduces a relaxed pruning method for multi-label rule discovery, enabling more expressive rules that better model label dependencies without increasing training time or reducing accuracy.

Contribution

It proposes a novel plug-in approach that relaxes pruning constraints, allowing the induction of larger multi-label heads in rule-based models.

Findings

More expressive multi-label rules are effectively discovered.

The approach maintains training efficiency and predictive performance.

Enhanced modeling of label dependencies improves interpretability.

Abstract

Being able to model correlations between labels is considered crucial in multi-label classification. Rule-based models enable to expose such dependencies, e.g., implications, subsumptions, or exclusions, in an interpretable and human-comprehensible manner. Albeit the number of possible label combinations increases exponentially with the number of available labels, it has been shown that rules with multiple labels in their heads, which are a natural form to model local label dependencies, can be induced efficiently by exploiting certain properties of rule evaluation measures and pruning the label search space accordingly. However, experiments have revealed that multi-label heads are unlikely to be learned by existing methods due to their restrictiveness. To overcome this limitation, we propose a plug-in approach that relaxes the search space pruning used by existing methods in order to introduce a bias towards larger multi-label heads resulting in more expressive rules. We further demonstrate the effectiveness of our approach empirically and show that it does not come with drawbacks in terms of training time or predictive performance.