Concept Tree Learner (CTL): An Incremental and Interpretable Symbolic Framework for Binary String Rule Induction
The Concept Tree Learner is introduced, an incremental and interpretable symbolic framework that induces logical concepts over binary strings from minimal labeled data and generalizes substantially better to unseen strings than both a classical entropy-based decision tree and the RIPPER rule learner.
Muhammed Tekin Ertekin, Burkay Genç
· Applied Sciences · 0 citations