activity
20152026
most citedRiver: machine learning for streaming data in Python

160 citations · 184 across the 16 of their papers we have counts for

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Showing cs.LGShow all

21 papers · 1 filter

cs.LG2026

Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information

Ben Halstead, Yun Sing Koh, Patricia Riddle +3

Streaming sources of data are becoming more common as the ability to collect data in real-time grows. A major concern in dealing with data streams is concept drift, a change in the…

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.LG20245 cited

A Probabilistic Framework for Adapting to Changing and Recurring Concepts in Data Streams

Ben Halstead, Yun Sing Koh, Patricia Riddle +2

The distribution of streaming data often changes over time as conditions change, a phenomenon known as concept drift. Only a subset of previous experience, collected in similar con…

cs.LG2024

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.LG2022

Green Accelerated Hoeffding Tree

Eva Garcia-Martin, Albert Bifet, Niklas Lavesson +2

State-of-the-art machine learning solutions mainly focus on creating highly accurate models without constraints on hardware resources. Stream mining algorithms are designed to run…