A Survey of Neural Trees: Co-Evolving Neural Networks and Decision Trees.

Abstract

Neural networks (NNs) and decision trees (DTs) are both popular models of machine learning, yet coming with mutually exclusive advantages and limitations. To bring the best of the two worlds, a variety of approaches are proposed to integrate NNs and DTs explicitly or implicitly. In this survey, these approaches are organized in a school which we term neural trees (NTs). This survey aims to present a comprehensive review of NTs and explore in detail how they enhance the model interpretability. Our first contribution is a detailed taxonomy of NTs, which characterizes the seamless integration and co-evolution of NNs and DTs. Subsequently, we analyze NTs in terms of their interpretability and performance and suggest potential solutions to the remaining challenges. Finally, this survey concludes with a discussion about other considerations like conditional computation and promising directions toward this field. A list of papers reviewed in this survey, along with their corresponding codes, is available at: https://github.com/ zju-vipa/awesome-neural-trees.