Western-OC2-Lab/PWPAE-Concept-Drift-Detection-and-Adaptation
Data stream analytics: Implement online learning methods to address concept drift and model drift in data streams using the River library. Code for the paper entitled "PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams" published in IEEE GlobeCom 2021.
This project helps operations engineers, security analysts, and researchers working with real-time IoT data streams maintain accurate analytics models. It takes continuous streams of sensor data or network traffic and applies online learning methods to automatically detect when the underlying data patterns change (concept drift). The output is an adapted model that remains effective even as the IoT environment evolves.
219 stars. No commits in the last 6 months.
Use this if your machine learning models for IoT data streams become less accurate over time due to changing data patterns, and you need an automated way to adapt them.
Not ideal if you are working with static, offline datasets where data distributions do not change, or if you need a solution for non-IoT-specific data streams.
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MIT
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Last pushed
Jun 05, 2023
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