A Massively Parallel SMC Sampler for Decision Trees

A Massively Parallel SMC Sampler for Decision Trees

Bayesian approaches to decision trees (DTs) using Markov Chain Monte Carlo (MCMC) samplers have recently demonstrated state-of-the-art accuracy performance when it comes to training DTs to solve classification problems. Despite the competitive classification accuracy, MCMC requires a potentially lon...

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Bibliographic Details
Main Authors: Efthyvoulos Drousiotis, Alessandro Varsi, Alexander M. Phillips, Simon Maskell, Paul G. Spirakis
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Algorithms
Subjects:
parallel algorithms
machine learning
Bayesian decision trees
sequential Monte Carlo samplers
Markov Chain Monte Carlo
shared memory
Online Access:https://www.mdpi.com/1999-4893/18/1/14
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