river and PWPAE-Concept-Drift-Detection-and-Adaptation
River is a foundational online machine learning library that PWPAE builds upon as a dependency to implement its ensemble-based concept drift detection framework.
About river
online-ml/river
🌊 Online machine learning in Python
When your data is constantly arriving in real-time, such as sensor readings, financial trades, or website clicks, River helps you build machine learning models that learn and adapt continuously. You feed in individual data points as they appear, and the system provides predictions or insights immediately, updating its understanding without needing to reprocess all past information. This is ideal for data scientists or machine learning engineers who need to deploy models that can react instantly to evolving data streams.
About PWPAE-Concept-Drift-Detection-and-Adaptation
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.
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