The article "Recent Advances in Decision Trees: An Updated Survey", published online in Artificial Intelligence Review, surpassed the 100-citation mark within two years, according to Web of Science (it was published online in October 2022).

Authored by Vinícius Garcia Costa (PESC doctoral student) and Prof. Carlos Pedreira (PESC), this article is part of Vinícius' doctoral thesis, which will be defended in 2025.

It is worth noting that there are 101 citations counted only in JCR journals."

Some details below or by clicking here.

Recent advances in decision trees: an updated survey
Authors: Vinícius G. Costa, Carlos E. Pedreira
Artificial Intelligence Review, Volume 56, Issue 5, Pages 4765 - 4800
Published: 10 October 2022
6456 Accesses, 136 Citations

Abstract
Decision Trees (DTs) are predictive models in supervised learning, known not only for their unquestionable utility in a wide range of applications but also for their interpretability and robustness. Research on the subject is still going strong after almost 60 years since its original inception, and in the last decade, several researchers have tackled key matters in the field. Although many great surveys have been published in the past, there is a gap since none covers the last decade of the field as a whole. This paper proposes a review of the main recent advances in DT research, focusing on three major goals of a predictive learner: issues regarding the fitting of training data, generalization, and interpretability. Moreover, by organizing several topics that have been previously analyzed in isolation, this survey attempts to provide an overview of the field, its key concerns, and future trends, serving as a good entry point for both researchers and newcomers to the machine learning community.

Congratulations to Vinícius and Prof. Pedreira!

Published on 11/29/2024.