Directional Decision Lists

TL;DR

This paper introduces a new family of interpretable, directionally oriented decision lists that are easier to train and can be applied to real-world problems like manufacturing process analysis.

Contribution

It proposes a novel class of decision lists with all rules oriented in the same direction, enhancing interpretability and training efficiency.

Findings

Easier training compared to general decision lists on simulated data

Effective in identifying problem symptoms in manufacturing processes

Demonstrates practical usability of the proposed model family

Abstract

In this paper we introduce a novel family of decision lists consisting of highly interpretable models which can be learned efficiently in a greedy manner. The defining property is that all rules are oriented in the same direction. Particular examples of this family are decision lists with monotonically decreasing (or increasing) probabilities. On simulated data we empirically confirm that the proposed model family is easier to train than general decision lists. We exemplify the practical usability of our approach by identifying problem symptoms in a manufacturing process.