Decision Tree
Decision trees are a fundamental machine learning model aiming to create interpretable predictive models by recursively partitioning data based on feature values. Current research emphasizes improving their accuracy, efficiency, and robustness, particularly within ensemble methods like random forests and gradient boosted trees, as well as exploring novel architectures like auto-regressive decision trees and their application in areas such as natural language processing. This ongoing work addresses challenges like vulnerability to adversarial attacks, handling noisy data, and optimizing for both accuracy and interpretability, impacting diverse fields from fraud detection to cybersecurity and beyond.
Papers
Cultivating Archipelago of Forests: Evolving Robust Decision Trees through Island Coevolution
Adam Żychowski, Andrew Perrault, Jacek Mańdziuk
Splitting criteria for ordinal decision trees: an experimental study
Rafael Ayllón-Gavilán, Francisco José Martínez-Estudillo, David Guijo-Rubio, César Hervás-Martínez, Pedro Antonio Gutiérrez
Evaluating the Efficacy of Vectocardiographic and ECG Parameters for Efficient Tertiary Cardiology Care Allocation Using Decision Tree Analysis
Lucas José da Costa, Vinicius Ruiz Uemoto, Mariana F. N. de Marchi, Renato de Aguiar Hortegal, Renata Valeri de Freitas
RL-LLM-DT: An Automatic Decision Tree Generation Method Based on RL Evaluation and LLM Enhancement
Junjie Lin, Jian Zhao, Yue Deng, Youpeng Zhao, Wengang Zhou, Houqiang Li