Streaming Gradient Boosted Regression (SGBR)

• Paper: Gradient boosted bagging for evolving data stream regression

News

• SGBR is now available on latest CapyMOA (Python) (13/09/2025)
• SGBR is now available on latest MOA (Java) (30/08/2025)

How to build the experiment setup

Go to source root.

Go to MOA source

cd moa

Build MOA

mvn clean install -DskipTests=true -Dmaven.javadoc.skip=true -Dlatex.skipBuild=true

Copy MOA jar to experiments folder

cp .//target/moa-2023.04.1-SNAPSHOT-jar-with-dependencies.jar ../exp/moa.jar

Run experiments

Setup

Create conda environment with Python and Java (assumes its installed in the system)

conda create -n SGBR python=3.9

go to exp folder

cd exp

create output folders

mkdir outout output_stats

go to dataset folder

cd RDatasets

Unzip dataset files

unzip hyperA.arff.zip

unzip DemandF.arff.zip

unzip NZEnergy.arff.zip

plsease contact authors for copy of SUP2I.arff, SUP3A.arff and SUP3G.arff

go level up (at exp)

cd ..

Run Wall time experiments

python benchmark_moa.py -f exp_config.json

Run periodic stats experiments

python benchmark_moa.py -f exp_config_overTime.json

Run hyperparameter search experiments

python benchmark_moa.py -f exp_config_SGBT_OZA_parameter_search.json

python benchmark_moa.py -f Plot_Hyper_LearningRate_FeatureP_twiny.py

Cite this work

@article{gunasekara2025gradient,
  title={Gradient boosted bagging for evolving data stream regression},
  author={Gunasekara, Nuwan and Pfahringer, Bernhard and Gomes, Heitor Murilo and Bifet, Albert},
  journal={Data mining and knowledge discovery},
  volume={39},
  number={5},
  pages={1--37},
  year={2025},
  doi={https://doi.org/10.1007/s10618-025-01147-x}
  publisher={Springer}
}

MOA (Massive Online Analysis)

MOA is the most popular open source framework for data stream mining, with a very active growing community (blog). It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. Related to the WEKA project, MOA is also written in Java, while scaling to more demanding problems.

http://moa.cms.waikato.ac.nz/

Using MOA

• Getting Started
• Documentation
• About MOA

MOA performs BIG DATA stream mining in real time, and large scale machine learning. MOA can be extended with new mining algorithms, and new stream generators or evaluation measures. The goal is to provide a benchmark suite for the stream mining community.

Mailing lists

• MOA users: http://groups.google.com/group/moa-users
• MOA developers: http://groups.google.com/group/moa-development

Citing MOA

If you want to refer to MOA in a publication, please cite the following JMLR paper:

Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (2010); MOA: Massive Online Analysis; Journal of Machine Learning Research 11: 1601-1604