Year: 2021
Type: preprint
Abstract: Machine-learning models are ubiquitous. In some domains, for instance, in medicine, the models' predictions must be interpretable. Decision trees, classification rules, and subgroup discovery are three broad categories of supervised machine-learning models presenting knowledge in the form of interpretable rules. The accuracy of these models learned from small datasets is usually low. Obtaining larger datasets is often hard to impossible. Pedagogical rule extraction methods could help to learn better rules from small data by augmenting a dataset employing statistical models and using it to learn a rule-based model. However, existing evaluation of these methods is often inconclusive, and they were not compared so far. Our framework PRELIM unifies existing pedagogical rule extraction techniques. In the extensive experiments, we identified promising PRELIM configurations not studied before.
Source: arXiv (Cornell University)
Institution: Karlsruhe Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W4225620669