unsupervised concept drift detection
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Updated
Aug 25, 2021 - Python
unsupervised concept drift detection
Efficient Multistream Classification using Direct DensIty Ratio Estimation
ForcingProcessor calculates NextGen catchment-averaged forcings from gridded sources like the National Water Model.
A comprehensive Python library for Elasticsearch management with both programmatic and CLI interfaces
CLI-based workflow tool for NextGen Water Modeling Framework simulations
Broad Ensemble Learning System (BELS)
Implementation tasks for multiple algorithms to process massive data. The algorithms are written in Python.
DynED is a novel ensemble construction and maintenance approach for data stream classification that dynamically balances the diversity and prediction accuracy of its components.
Balancing Efficiency vs. Effectiveness and Providing Missing Label Robustness in Multi-Label Stream Classification
Puro - Highly configurable data streams in Python 3.x
A simple datastream built with Apache Kafka and Python running on Docker.
Python wrapper for the Datastream Web Services API (DSWS)
Fake-Heart-Sensor-Data-Using-Python-and-Kafka is a GitHub project that provides a simple and easy-to-use way to generate simulated heart sensor data using Python and Kafka. This project is ideal for developers who want to test their applications with realistic heart sensor data or simulate a data stream for research purposes.
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