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
Implements single-pass learning algorithms optimized for concept drift and streaming data, supporting linear models, decision trees, anomaly detection, and time series forecasting without requiring historical data access. Features composable pipelines with integrated feature preprocessing, drift detection, and progressive validation for production-like event-driven workflows. Designed for compatibility with Python's broader ML ecosystem while prioritizing per-sample efficiency over batch processing throughput.
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.
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