TNT: An Interpretable Tree-Network-Tree Learning Framework Using Knowledge Distillation

TNT: An Interpretable Tree-Network-Tree Learning Framework Using Knowledge Distillation

Deep Neural Networks (DNNs) usually work in an end-to-end manner. This makes the trained DNNs easy to use, but they remain an ambiguous decision process for every test case. Unfortunately, the interpretability of decisions is crucial in some scenarios, such as medical or financial data mining and de...

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Bibliographic Details
Main Authors: Jiawei Li, Yiming Li, Xingchun Xiang, Shu-Tao Xia, Siyi Dong, Yun Cai
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Entropy
Subjects:
deep neural networks
James–Stein Decision Trees
distillable gradient boosted decision tree
interpretable machine learning
knowledge distillation
Online Access:https://www.mdpi.com/1099-4300/22/11/1203

Internet

https://www.mdpi.com/1099-4300/22/11/1203

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