Neural Regression Trees

10/01/2018
by   Shahan Ali Memon, et al.
0

Regression-via-Classification (RvC) is the process of converting a regression problem to a classification one. Current approaches for RvC use ad-hoc discretization strategies and are suboptimal. We propose a neural regression tree model for RvC. In this model, we employ a joint optimization framework where we learn optimal discretization thresholds while simultaneously optimizing the features for each node in the tree. We empirically show the validity of our model by testing it on two challenging regression tasks where we establish the state of the art.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/12/2022

TreeExplorer: a coding algorithm for rooted trees with application to wireless and ad hoc routing

Routing tables in ad hoc and wireless routing protocols can be represent...
research
09/02/2019

Bayesian Neural Tree Models for Nonparametric Regression

Frequentist and Bayesian methods differ in many aspects, but share some ...
research
09/23/2022

Revocation management in vehicular ad-hoc networks

This paper describes a solution for the efficient management of revocati...
research
10/09/2020

Learning Binary Trees via Sparse Relaxation

One of the most classical problems in machine learning is how to learn b...
research
02/23/2018

Model Trees for Identifying Exceptional Players in the NHL Draft

Drafting strong players is crucial for the team success. We describe a n...
research
02/04/2022

Backpropagation Neural Tree

We propose a novel algorithm called Backpropagation Neural Tree (BNeural...
research
11/02/2020

A better method to enforce monotonic constraints in regression and classification trees

In this report we present two new ways of enforcing monotone constraints...

Please sign up or login with your details

Forgot password? Click here to reset