Article ID Journal Published Year Pages File Type
13429332 Information Sciences 2020 22 Pages PDF
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
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