Directional Decision Lists
This work addresses the need for interpretable and efficient machine learning models, particularly in domains like manufacturing, though it appears incremental as it builds on existing decision list frameworks.
The paper tackles the challenge of learning interpretable decision lists efficiently by introducing a novel family of directional decision lists with rules oriented in the same direction, such as monotonically decreasing probabilities, and demonstrates on simulated data that this model family is easier to train than general decision lists, with practical application shown in identifying problem symptoms in a manufacturing process.
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.