Directional decision lists
By Marc Goessling, Shan Kang. 2015
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.
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- Elaine Angelino
- Daniel Alabi
- Nicholas Larus-Stone
- Cynthia Rudin
- Alexey Ignatiev
- Tong Wang
- S. Lo Piano
- Nina Narodytska
Citations in the corpus
- Learning Certifiably Optimal Rule Lists (2017, methodology): “Methods for decision list learning construct rule lists iteratively in a greedy way (Rivest, 1987; Sokolova et al., 2003; Marchand and Sokolova, 2005; Rudin et al., 2013; Goessling and Kang, 2015); these too have no guarantee of optimality, and tend not to produce optimal rule lists in general.”