Drift-Lens: an Unsupervised Drift Detection Framework for Deep Learning Classifiers on Unstructured Data

🌊 Online machine learning in Python

Algorithms for outlier, adversarial and drift detection

A General Toolkit for Online Learning Approaches

Enhancing electricity price forecasting accuracy using a hybrid model combining GRU and XGBoost with detection-informed retraining for concept drift.

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

Algorithms proposed in the following master dissertation: OLIVEIRA, Gustavo Henrique Ferreira de Miranda. Previsão de séries temporais na presença de mudança de conceito: uma abordagem baseada em PSO. 2018. Dissertação de Mestrado. Universidade Federal de Pernambuco.

Music album popularity prediction classic ML model showcasting MLOps, versioning, feature selection, cross valdiation and concept drift.

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 Convol…

Credit Card Fraud Detection

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

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

📖These are the concept drift datasets we made, and we open-source the data and corresponding interfaces. Welcome to use them for free if there is a need.

Drift Lens Demo

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

CADM+: Confusion-based Learning Framework With Drift Detection and Adaptation for Real-time Safety Assessment

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

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.

Concept Drift Detection and Adaptation Methods - Reference Codes and Papers

Balancing Efficiency vs. Effectiveness and Providing Missing Label Robustness in Multi-Label Stream Classification