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cs.LG2025

Simulation-Driven Railway Delay Prediction: An Imitation Learning Approach

Clément Elliker, Jesse Read, Sonia Vanier +1

Reliable prediction of train delays is essential for enhancing the robustness and efficiency of railway transportation systems. In this work, we reframe delay forecasting as a stoc…

cs.LG2025

Binary Split Categorical feature with Mean Absolute Error Criteria in CART

Peng Yu, Yike Chen, Chao Xu +2

In the context of the Classification and Regression Trees (CART) algorithm, the efficient splitting of categorical features using standard criteria like GINI and Entropy is well-es…

cs.LG2025

Branches: Efficiently Seeking Optimal Sparse Decision Trees with AO*

Ayman Chaouki, Jesse Read, Albert Bifet

Decision Tree (DT) Learning is a fundamental problem in Interpretable Machine Learning, yet it poses a formidable optimisation challenge. Practical algorithms have recently emerged…

cs.LG2024

Label Cluster Chains for Multi-Label Classification

Elaine Cecília Gatto, Felipe Nakano Kenji, Jesse Read +3

Multi-label classification is a type of supervised machine learning that can simultaneously assign multiple labels to an instance. To solve this task, some methods divide the origi…

cs.LG2024

Online Learning of Decision Trees with Thompson Sampling

Ayman Chaouki, Jesse Read, Albert Bifet

Decision Trees are prominent prediction models for interpretable Machine Learning. They have been thoroughly researched, mostly in the batch setting with a fixed labelled dataset,…