YaDT: yet another decision tree builder

37 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Local Rule-Based Explanations of Black Box Decision Systems (2018, methodology): “LORE has been developed in Python 9 , using, for the genetic neighborhood generation, the deap library [13], and for decision tree induction (the interpretable predictor), the yadt system [32], which is a C4.5 implementation with multi-way splits of categorical attributes.”
Subtree Replacement in Decision Tree Simplification (2012, methodology): “The current availability of efficient (sequential and parallel) implementations of decision tree induction algorithms [1, 14, 22, 24] makes post-processing techniques worth to be investigated both for efficiency and effectiveness.”
On The Stability of Interpretable Models (2018, methodology): “5 [21] as implemented by the computationally efficient YaDT system4 [22].”
Complete Search for Feature Selection in Decision Trees (2019, methodology): “All procedures described in this paper are implemented by extending the YaDT system (Ruggieri, 2002, 2004; Aldinucci et al., 2014).”