Western-OC2-Lab / OASW-Concept-Drift-Detection-and-Adaptation

An online learning method used to address concept drift and model drift. Code for the paper entitled "A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams" published in IEEE Internet of Things Magazine.

Date Created 2021-04-02 (3 years ago)

Commits 38 (last one about a year ago)

Stargazers 51 (0 this week)

Watchers 3 (0 this week)

Forks 17

License mit

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RepositoryStats indexes 622,366 repositories, of these Western-OC2-Lab/OASW-Concept-Drift-Detection-and-Adaptation is ranked #478,515 (23rd percentile) for total stargazers, and #435,531 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #12,711/18,620.

Western-OC2-Lab/OASW-Concept-Drift-Detection-and-Adaptation is also tagged with popular topics, for these it's ranked: machine-learning (#6,845/8311), iot (#1,016/1206), anomaly-detection (#263/348), hyperparameter-optimization (#108/126)

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All Topics

iot svm drift nsl-kdd xgboost lightgbm data-stream concept-drift random-forest change-detector drift-detection online-learning machine-learning anomaly-detection iot-data-analytics real-time-analytics bayesian-optimization intrusion-detection-system hyperparameter-optimization particle-swarm-optimization

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4 commits on the default branch (main) since jan '22

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The only known language in this repository is Jupyter Notebook

updated: 2025-02-07 @ 09:57am, id: 353888249 / R_kgDOFRfn-Q