concept-drift

🌊 Online machine learning in Python

Algorithms for outlier, adversarial and drift detection

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

Implementation/Tutorial of using Automated Machine Learning (AutoML) methods for static/batch and online/continual learning

Frouros: an open-source Python library for drift detection in machine learning systems.

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.

Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications

The Tornado 🌪 framework, designed and implemented for adaptive online learning and data stream mining in Python.

This is an official PyTorch implementation of the NeurIPS 2023 paper 《OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling》

Algorithms for detecting changes from a data stream.

AutoGBT is used for AutoML in a lifelong machine learning setting to classify large volume high cardinality data streams under concept-drift. AutoGBT was developed by a joint team ('autodidact.ai') from Flytxt, Indian Institute of Technology Delhi and CSIR-CEERI as a part of NIPS 2018 AutoML for Lifelong Machine Learning Challenge.

The official API of DoubleAdapt (KDD'23), an incremental learning framework for online stock trend forecasting, WITHOUT dependencies on the qlib package.

MemStream: Memory-Based Streaming Anomaly Detection

A curated list of awesome open source tools and commercial products for monitoring data quality, monitoring model performance, and profiling data 🚀

CinnaMon is a Python library which offers a number of tools to detect, explain, and correct data drift in a machine learning system

Online and batch-based concept and data drift detection algorithms to monitor and maintain ML performance.

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.

Repository for the AdaptiveRandomForest algorithm implemented in MOA 2016-04

concept drift datasets edited to work with scikit-multiflow directly

This repository includes code for the AutoML-based IDS and adversarial attack defense case studies presented in the paper "Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis" published in IEEE Transactions on Network and Service Management.