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20192022
most citedEnhancing Decision Tree based Interpretation of Deep Neural Networks through L1-Orthogonal Regularization

1 citations · 1 across the 2 of their papers we have counts for

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cs.LG2022

Mixture of Decision Trees for Interpretable Machine Learning

Simeon Brüggenjürgen, Nina Schaaf, Pascal Kerschke +1

This work introduces a novel interpretable machine learning method called Mixture of Decision Trees (MoDT). It constitutes a special case of the Mixture of Experts ensemble archite…

cs.LG2019★ 1 cited

Enhancing Decision Tree based Interpretation of Deep Neural Networks through L1-Orthogonal Regularization

Nina Schaaf, Marco F. Huber, Johannes Maucher

One obstacle that so far prevents the introduction of machine learning models primarily in critical areas is the lack of explainability. In this work, a practicable approach of gai…