Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
13429332 | Information Sciences | 2020 | 22 Pages |
Abstract
We empirically demonstrate that Classy selects small probabilistic rule lists that outperform state-of-the-art classifiers when it comes to the combination of predictive performance and interpretability. We show that Classy is insensitive to its only parameter, i.e., the candidate set, and that compression on the training set correlates with classification performance, validating our MDL-based selection criterion.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Hugo M. Proença, Matthijs van Leeuwen,