Deep Decision Forest

From ISLAB/CAISR
Title Deep Decision Forest
Summary Designing a deep model that uses decision trees instead of artificial neurons
Keywords deep decision forest, explainable AI
TimeFrame Autumn 2024
References
Prerequisites
Author
Supervisor Sławomir Nowaczyk
Level Master
Status Open


The success of Deep Learning is largely attributed to the ability to create/extract "hierarchical features" from the data. This is successful using artificial neurons or perceptrons, and the backpropagation algorithm.

The price is, however, the very large size of the model, which translates into computational costs and a "black-box" nature, or lack of explainability.

This project aims to explore ways to train a deep model using a chain of decision trees, like layers in a neural network. It promises to significantly reduce model complexity and increase interpretability.